{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_ActDist\n",
    "This is a demo for visualizing the class distribution of top-k images which activate the Neurons of a Neuron network most.\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py --save_folder_name badnet_demo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71b7087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib\n",
    "from matplotlib.patches import Rectangle, Patch\n",
    "\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb719c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697f71ae",
   "metadata": {},
   "source": [
    "### Step 2: Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "872af063",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "create mix dataset with length:  10000\n",
      "max_num_samples is given, use sample number limit now.\n",
      "subset mix dataset with length:  4997\n",
      "Create visualization dataset with \n",
      " \t Dataset: mixed \n",
      " \t Number of samples: 4997  \n",
      " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "selected_classes = np.arange(args.num_classes)\n",
    "\n",
    "# Select classes to visualize\n",
    "if args.num_classes>args.c_sub:\n",
    "    selected_classes = np.delete(selected_classes, args.target_class)\n",
    "    selected_classes = np.random.choice(selected_classes, args.c_sub-1, replace=False)\n",
    "    selected_classes = np.append(selected_classes, args.target_class)\n",
    "\n",
    "# keep the same transforms for train and test dataset for better visualization\n",
    "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n",
    "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n",
    "\n",
    "# Create dataset\n",
    "if args.visual_dataset == 'mixed':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_mix_dataset(bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_train':\n",
    "    clean_train_with_trans = result_attack[\"clean_train\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_test':\n",
    "    clean_test_with_trans = result_attack[\"clean_test\"]\n",
    "    visual_dataset = generate_clean_dataset(clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_train':  \n",
    "    bd_train_with_trans = result_attack[\"bd_train\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_test':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_bd_dataset(bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "else:\n",
    "    assert False, \"Illegal vis_class\"\n",
    "\n",
    "print(f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)}  \\n \\t Selected classes: {selected_classes}')\n",
    "\n",
    "# Create data loader\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "    visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n",
    ")\n",
    "\n",
    "# Create denormalization function\n",
    "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n",
    "    if isinstance(trans_t, transforms.Normalize):\n",
    "        denormalizer = get_dataset_denormalization(trans_t)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 3: Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b8b67ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "\n",
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 4: Get Activation Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94612903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualize Top-500 Samples from 4997 Samples.\n",
      "Collecting features from module Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "Collecting features from module Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "Collecting features from module Linear(in_features=512, out_features=10, bias=True)\n"
     ]
    }
   ],
   "source": [
    "module_dict = dict(model_visual.named_modules())\n",
    "module_names = module_dict.keys()\n",
    "\n",
    "# Plot Conv2d or Linear\n",
    "module_visual = [i for i in module_dict.keys() if isinstance(\n",
    "    module_dict[i], torch.nn.Conv2d) or isinstance(module_dict[i], torch.nn.Linear) or isinstance(module_dict[i], torch.nn.BatchNorm2d)]\n",
    "\n",
    "poi_indicator = np.array(get_poison_indicator_from_bd_dataset(visual_dataset))\n",
    "labels = np.array(get_true_label_from_bd_dataset(visual_dataset))\n",
    "\n",
    "\n",
    "df = None\n",
    "\n",
    "# decide the number of images to compute the distribution\n",
    "num_image = int(len(visual_dataset)/len(selected_classes)) \n",
    "if poi_indicator.sum() > 0:\n",
    "    num_image = poi_indicator.sum()\n",
    "    # regard the poisoned images as a class with label args.num_classes\n",
    "    labels[poi_indicator==1] = args.num_classes\n",
    "    \n",
    "print(f'Visualize Top-{num_image} Samples from {len(visual_dataset)} Samples.')\n",
    "\n",
    "label_set = np.unique(labels)\n",
    "label_set.sort()\n",
    "\n",
    "max_num_neuron = 0\n",
    "for module_name in module_visual:\n",
    "    target_layer = module_dict[module_name]\n",
    "    print(f'Collecting features from module {target_layer}')\n",
    "\n",
    "    features, labels, poi_indicator = get_features(\n",
    "        args, model_visual, target_layer, data_loader, reduction='sum', activation= None)\n",
    "\n",
    "    # set the poisoned images as a class with label args.num_classes for each iteration. \n",
    "    # this can be skipped if shuffle is set to False.\n",
    "    labels[poi_indicator==1]=args.num_classes\n",
    "    total_neuron = features.shape[1]\n",
    "    max_num_neuron = np.max([max_num_neuron, total_neuron])\n",
    "    top_indx = np.argsort(-features, axis=0)[:num_image, :]\n",
    "    top_pred = np.array(labels)[top_indx]\n",
    "\n",
    "    for neuron_i in range(total_neuron):\n",
    "        base_row = {}\n",
    "        base_row['layer'] = module_name\n",
    "        base_row['Neuron'] = neuron_i\n",
    "        for i in range(len(label_set)):\n",
    "            base_row[f'percent_{i}'] = np.sum(\n",
    "                top_pred[:, neuron_i] == label_set[i])/num_image\n",
    "        if df is None:\n",
    "            df = pd.DataFrame.from_dict([base_row])\n",
    "        else:\n",
    "            df.loc[df.shape[0]] = base_row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3760b9",
   "metadata": {},
   "source": [
    "### Step 5: Show the Activation Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "81bbb857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ploting conv1\n",
      "ploting layer1.0.bn1\n",
      "ploting layer1.0.conv1\n",
      "ploting layer1.0.bn2\n",
      "ploting layer1.0.conv2\n",
      "ploting layer1.1.bn1\n",
      "ploting layer1.1.conv1\n",
      "ploting layer1.1.bn2\n",
      "ploting layer1.1.conv2\n",
      "ploting layer2.0.bn1\n",
      "ploting layer2.0.conv1\n",
      "ploting layer2.0.bn2\n",
      "ploting layer2.0.conv2\n",
      "ploting layer2.0.shortcut.0\n",
      "ploting layer2.1.bn1\n",
      "ploting layer2.1.conv1\n",
      "ploting layer2.1.bn2\n",
      "ploting layer2.1.conv2\n",
      "ploting layer3.0.bn1\n",
      "ploting layer3.0.conv1\n",
      "ploting layer3.0.bn2\n",
      "ploting layer3.0.conv2\n",
      "ploting layer3.0.shortcut.0\n",
      "ploting layer3.1.bn1\n",
      "ploting layer3.1.conv1\n",
      "ploting layer3.1.bn2\n",
      "ploting layer3.1.conv2\n",
      "ploting layer4.0.bn1\n",
      "ploting layer4.0.conv1\n",
      "ploting layer4.0.bn2\n",
      "ploting layer4.0.conv2\n",
      "ploting layer4.0.shortcut.0\n",
      "ploting layer4.1.bn1\n",
      "ploting layer4.1.conv1\n",
      "ploting layer4.1.bn2\n",
      "ploting layer4.1.conv2\n",
      "ploting linear\n"
     ]
    },
    {
     "data": {
      "image/png": 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7cr/73S8PeMADUkpZuybAbjKCGeA8+uQnP5m3vOUt+Zu/+ZuUUnLLLbeklJKXvGTj3KRf+ZVfuWF78B/hg9tzc3P54z/+47ztbW/Lne50pzzxiU/MF7/4xSTJ7W53u7XjL7300nzpS19K0vmK8A033HBRjrLZrs7PfvazN0wv0qvRMPrreckll6x9pfaSSy5Zq+1OTvcs+9Va8wu/8At52tOeNnTO25rx8fF8/OMfz80337xp3+Dn4WL0sIc9bMuv6B8/fjw333xzTp06ldvd7na5733v2+i9va364Ac/mEsvvTRf/dVfve3noTd/93/4D/9hQ/tNN9100b87/fWh42J/5rulv24//MM/nGc+85n5gz/4g1x11VX5oz/6o9Rat/ycXSwuvfTSPPGJT8wTn/jEXHHFFXnNa16TJGv/G9//76dBw/zv+m1d/3uyb9++of9N9ehHPzqnTp3KJz/5yQ0dz7dlO80Fv5NXvepV+Yu/+Iv8/u//fh71qEft+E27ZP1dTNbfx1prvumbvmnTIIqL/dtNwIXJCGaA8+i3fuu38oIXvCAf+tCHctNNN2VpaSn3u9/9Tjs36Nvf/vb8/d//fW699db8xm/8Rr7hG75hw/5Pf/rTudvd7pY73elOed/73pcbbrjhtFme+tSn5hd+4RfWti+mf4xuV+dbb70173nPe7K6uppPfepT+ZM/+ZO1c+5yl7uszTP5mMc8JvPz8/n4xz+eW265Ja973evWvi57Jh7/+Mfn+PHjSZL3v//9+Yd/+Ic86EEPSpK8+c1vzic/+cl84QtfyJve9KZcddVV217naU97Wo4ePZp/+Zd/WbvW5z73uaHrcVvwvve9L7fccsum6WIGPeEJT8iv//qvJ+lMK9Gb1/C27slPfnJWV1c3zJ/+rne9Kx/60Ify1V/91bnd7W6XkydP5kMf+lCSje/rxermm2/Oi1/84rz0pS9NKWXbz8PTnva0HDt2bO0bGMvLy/nYxz62l9HPi8H6bPf3zlVXXZU3vOENSZL3vOc9+Zu/+Zu9jH1ePf7xj89v/MZv5JZbbsnNN9+cP/3TP81jHvOYXHXVVfnt3/7t3Hrrrfmnf/qnzM3N7XXU8+Lrv/7r134nwfHjx/P4xz9+y+MWFxdzxRVX5Id+6Ify6Ec/Ou973/su6s/Z3/3d320Yjf3Od74z97nPfc74/N/8zd/MrbfemsXFxXzwgx9c+9/7i91973vf/NVf/VWSzlRWf//3f5+k879nv/mbv5lPfOITSbJhioynP/3pa/8B42L+37D+v4/n5uZyj3vcI3e96103HLO4uJjHPvaxmZ2dzWWXXZalpaUd//24lcc97nH5sz/7s9x4Y+eXY37uc5/L+9///jz4wQ/OTTfdlMXFxSQZ6lt8AE0ZwQxwHr3uda/b8AvjkuS5z31ufvqnf3rH8x796EfnpS99aW688cY86UlPytTU1Ib9T3/60/OqV70qD3nIQ/KgBz3ojL4S/spXvjIveclLcuWVV+ZLX/pSnvCEJ+RVr3rV8D/UBWi7Or/+9a/Pt37rt+byyy/P/e53vzziEY9Y2//d3/3defrTn5573eteOXnyZK699to86UlPSq01z3zmM/PsZz/7jO//Pd/zPZmens4VV1yRffv25frrr18befKYxzwmz33uc/PhD384z3/+87edfzlJ/t2/+3e56aab8shHPjK11lx22WVr82VeDPrnCq215jWvec1pv3o7PT2dF73oRXnIQx6ShzzkIVvOW3xbVErJG9/4xvzH//gf8zM/8zO5wx3ukPve9745cuRIvu/7vi9XXHFFWq1WHvzgBydJ7n73u+eqq67K5Zdfnmc84xn52Z/92T3+Cc6N3jvxL//yL9m3b19e8IIX5Ad+4AeSbP95eOpTn5r3vve9GR8fT9L5uvuv/dqvnfZdui3aqT7b/b3zPd/zPXnhC1+Yhz70oXnwgx+chz3sYdm/f/8e/yTnx9TUVN72trfl4Q9/eEop+a//9b/mnve8Z5773OfmT/7kT/LQhz40Bw4cyCMf+cgvi5r8wi/8Ql70ohflZ3/2Z3PZZZfluuuu2/K4V7ziFTl58mQuueSSPOxhD8sznvGM3P72t9/yc3YxjJ7/7Gc/m+/93u9dmw5kdHQ0r371q8/4F/Z93dd9XR7zmMdkZWUlr3rVqy7Kb4Zt5bnPfW5e+9rX5mEPe1ge+9jH5oEPfGCSzjdyfvRHfzQTExO59NJL84hHPGJtypGkM83GZz7zmUxOTuYP/uAPcsc73nGPfoLdc+TIkXzXd31XrrzyytzpTndaGxHf72Uve1k+8IEPpNaapzzlKXn4wx+eBz/4wdv++3Erl112Wa6//vp8+7d/e1ZXV5MkP/mTP5kHPvCBefWrX51nPvOZudOd7pTHP/7xF3WHPnBhKFvNdQhwsTp16lS9rXVIzc3N5ed+7ucu6t9MDsDuuOWWW/Iv//IvucMd7pDFxcV84zd+Y/7u7/4uX/EVX7HX0fbUZz/72dz5znfOJz7xiTzmMY/Jn/3Zn+We97znXsfiNubQoUN51rOetekXL8NtxalTp/Kf//N//tUkP3HixInlvc4D3HYZwQwAABepz3/+83nSk56Uf/mXf0mtNb/0S7/0Zd+5nCTPetaz8qlPfSr//M//nB//8R/XuQwAcBZ0MANc4Hq/dAYAhnWXu9wl73jHO/Y6xgXny2XeZXZX/9QPAPDlzC/5AwAAAACgER3MwJebeuutt+51BgAAgD1z6623xu/kAs4VHczAl5VLLrnkff/4j/94i05mAADgy9Gtt96aj370o7d+8Ytf/Hi3SU8zcFbMwQx8Wbn11luf+tGPfvQtH/nIRx5QStnrOAAAAOdVrTVf/OIXP3n99df/7ySfS/LJvc4E3LYVX4kAvhxNTk4+IsmhJPv3OAoAAMD5VpN8NMnREydOLO11GOC2TQczAAAAAACNmIMZAAAAAIBGdDADAAAAANCIDmYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGdDADAAAAANCIDmYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGdDADAAAAANCIDmYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGdDADAAAAANCIDmYAAM5IKeVVpZQfP0fX+rpSymdLKZd2t+dKKf/uXFy7e73/U0p54bm63hD3/clSysdLKf94vu8NAAB7QQczAAAppdxUSvlCKeUzpZRPlVL+vJTy4lLK2r8Xa60vrrX+lzO81jfudEyt9R9qrXeutd5yDrIfKaX82sD1n1Frfc3ZXnvIHF+X5P9J8tBa6z0H9n1Ht0P9s90639q3/dlzmOFIKeVf+q9dSrl/3/6xUsqpUsrnu8uxvn2llPIzpZRPdP/8TCmlbHOfJ5ZSPnyucgMAcNulgxkAgJ5/U2u9S5L7JLk2yQ8l+dVzfZNSyr5zfc0LxNcl+USt9WODO2qtx7sd6ndO8owkH+ltd9vOpd/ov3at9YNJUkr5iiS/m+TXktwtyWuS/G63PUm+O8lzkjw8yZVJ/k2S/3COswEAcJHRwQwAwAa11k/XWk8k+bYkLyylXJ4kpZTrSyk/2V2/Rynl97qjnT9ZSvn/SimXlFL+Vzodrf+7O3r25aWU+5ZSainlcCnlH5K8pa+tv7P5X5dS3l5KWSml/G4p5au699o0WrY3SrqU8vQkM0m+rXu/v+7uX5tyo5vrx0opHyqlfKyU8tpSyv7uvl6OF5ZS/qE7vcWPblebUsr+7vk3d6/3Y93rf2OSNye5VzfH9Wda71LKQ7p5P1VK+dtSymTfvuu7U5O8uTu6fL6Ucp8zvfaAJybZl+QVtdbVWusrk5QkT+7uf2GS/1Zr/XCtdTnJf0ty6Ax/hrnu9CB/3v35/3cp5e6llOPd5/mXpZT79h3//5ZSlrr7TpVSHt+3746llNeUUv5vKeW93Xfow33771VK+e3uM/j7Usr39e17TCnlHd3r/lMp5ecb1goAgDOkgxkAgC3VWt+e5MNJHr/F7v+nu++yJF+TTidvrbW+IMk/pDMa+s611v/ad85Ekockedo2t/zOJN+V5GuTfCnJK88g4x8muSbro3YfvsVhh7p/npTk/knunOR/DBzzDUkelOQpSf5/pZSHbHPLX0iyv3udiW7mF9Va/zgbRyYfOl32JCml3C7J/07STvLVSb43yfFSyoP6DvuOJP8lyT2SvDPJ8dNc9t90O/3/tpQy3df+sCTvqrXWvrZ3ddt7+/+6b99f9+07E1cneUGSkST/OsnbklyX5KuSvDfJT/Qd+5dJxrr7fj3Jb5ZS7tDd9xNJ7ptOjb8pyfN7J5XOlC3/u5ttJJ3n9R9LKb136v9N8v/WWu/azfCGIfIDANCADmYAAHbykXQ6AQf9Szodwfeptf5LrfX/G+i43MqRWuvnaq1f2Gb//6q1vrvW+rkkP57kW0v3lwCepe9I8vO11g/WWj+b5EeSXD0wevo/11q/UGv963Q6Lzd1VHezXJ3kR2qtn6m13pTOKN8XnEW2x6XT4X1trfWfa61vSfJ7Sb6975jfr7X+aa11NcmPJhkvpRzY5npvSKcT/7Ik/z6dzvLete6c5NMDx386yV222f/pJHfebh7mLVxXa12stX46yf9Jslhr/eNa65eS/GaSR/QOrLX+Wq31E7XWL9Va/1uS26fTwZ8k35rkmlrr/621fjgb/0PDo5NcVmud7dbrg0l+OZ3nknTey9FSyj1qrZ+ttd5whtkBAGhIBzMAADsZSfLJLdp/NsmNSdqllA+WUn74DK61NMT+DyW5XTqjds/WvbrX67/2vnRGXvf8Y9/659PpbB10j26mwWuNnGW2pVrrrTtcc60u3Q7yT6YzFcdMWf9Ffq/q7n9PrfUjtdZbaq1/ns6I3n/bPf2zSe46cP+7JvnMNvvvmuSzZ/AfDnr+qW/9C1tsr9W0lPKD3ekvPl1K+VQ6o8J7z/pe2fgu9K/fJ52f/VO9P+mMnu89y8NJHpjkfd1pOZ51htkBAGhIBzMAAFsqpTw6nY7Otw7u647g/X9qrfdPMpnkB0opT+nt3uaSp+uo7B+V+3XpjEb9eJLPJblTX65L0xmhe6bX/Ug6HZP91/5SNnaAnomPdzMNXmt5yOsMZjvQnfphu2uu1aWUcud0RpR/pNZ6Td8v8nvxNtev6cyznCR/m+TKgRHJV3bbe/v7R24/vG/fOdOdb/nl6YxUvlut9V+lM1q6l+ujSe7dd0r/e7GU5O9rrf+q789daq3fnCS11g/UWr89nelGfibJb5VSvvJc/wwAAKzTwQwAwAallLt2R36+Psmv1Vr/ZotjnlVKGe12Vn46yS1JeqNw/ymd+XOH9fxSykNLKXdKMpvkt2qttyR5f5I7lFKe2Z2z+MfSmVKh55+S3Hegk7bf65L8p1LK/bodtL05m780TLhuljck+alSyl26v2zvB5L82jDXGfAX6YyYfnkp5XallCcm+Tfp1L7nm0sp31BK+Yp05mK+oda65WjwUsqzSyl3Kx2PSfJ9SX63u3sunef0faWU25dSXtptf0t3+dp0/kPBSCnlXunMs339Wfxs27lLOh38NyfZV0r5/2XjyOk3JPmR7s8xkuSlffvenuQzpZQf6v4ywEtLKZd3/2NISinPL6Vc1h0R/qnuOf2jwwEAOMd0MAMA0PO/SymfSWeU6I8m+fkkL9rm2Ack+eN0plV4W5JfqrWe7O776SQ/1p3C4AeHuP//SqdD8x+T3CGdztF05/T9niS/ks7I3s+l8wsGe36zu/xEKeWvtrjuse61/zTJ3yf5Yjq/TK+J7+3e/4PpjOz+9e71G6m1/nM6HcrPSGeE9C8l+c5a6/v6Dvv1dH7x3SeTPCp9v/RuC1enM3XJZ9LpMP6ZWutr+u71nHR+MeGn0vmFis/ptifJ/0znF+j9TZJ3J/n9btu59kdJ/jCd/3DwoXSeR3+H+Ww6z/fv03nHfivJavdnuCXJs9L5BYF/n07NfiWdKTaS5OlJ/raU8tl0pge5eoc5vwEAOAfKmU+pBgAAnE+llOuTfLjW+mN7nWWvlFKm0+kontjrLAAAbGYEMwAAcMEopXxtKeWqUsolpZQHpTNVxxv3OhcAAFvbt9cBAAAA+nxFOlNz3C+dqTxen87UIQAAXIBMkQEAAAAAQCOmyAAAAAAAoBEdzAAAAAAANHKbnoP5Hve4R73vfe+71zEAAAAAAC5qp06d+nit9bLB9tt0B/N973vfvOMd79jrGAAAAAAAF7VSyoe2ar9NdzADAAAAF5elIzNZPpzMdbdXMrbxgNXxznL+9NcaWdy4fV17auiBahdanhuWZtbWTwxm2U4vY7KWc2Qxmeg75FB7Kkka1WfNxPbHLY92arhWv9XxDTU7uJjsz0KSZKo9u9a+V/VJNtborOuzRW2Oj27cXlmdXlufPrZw2mtPtWdv83lmlt4w1PGb9H3+phc3ZzxwzeTZXZ8zYg5mAAAAAAAa0cEMAAAAAEAjOpgBAAAAAGik1Fr3OkNjrVar+iV/AAAAAAC7q5RyqtbaGmw3ghkAAAAAgEZ2tYO5lHJTKeVvSinvLKW8o9v2vFLK35ZSbi2ltAaO/5FSyo2llL8rpTxtN7MBAAAAAHB29p2Hezyp1vrxvu13J/mWJP+z/6BSykOTXJ3kYUnuleSPSykPrLXech4yAgAAABeApSMzSZLlw8lct20lY8nqeDLf2R5Z3Pkak1nIVHt2y33DTrV5oebZYGLrY5dHO5lXMpaRo+OZSLI/C9tee6o9e27ybJNpebSznOtur6xOr++c79SxP+M5y7NNfZLk+Oh6loPHdqE+J2dy9MB0Ro7tGGNbF3ueG5a2eX/6zHWXG96XrpFjneXkFrma5KGZ8z5FRq31vbXWv9ti17OTvL7Wulpr/fskNyZ5zPlNBwAAAADAmdrtDuaapF1KOVVK+e7THDuSZKlv+8Pdtg1KKd9dSnlHKeUdN9988zmMCgAAAADAMHa7g/kbaq2PTPKMJC8ppTzhbC9Ya311rbVVa21ddtllZ58QAAAAAIBGSq31/NyolCNJPltr/bnu9lySH6y19n75348kSa31p7vbf5TkSK31bdtds9VqVXOpAAAAAADsrlLKqVpra7B910Ywl1K+spRyl956kqem8wv+tnMiydWllNuXUu6X5AFJ3r5b+QAAAAAAODv7dvHaX5PkjaWU3n1+vdb6h6WUqSS/kOSyJL9fSnlnrfVptda/LaW8Icl7knwpyUtqrbfsYj4AAADgArN0ZCaZSJZHO9tzSVYylqyO73zifDKyuP3uySxkqj2bYb8JfUHmSbJ8ePO+tWzJWr6Dx5JD7akzvn7T+vT01ymDeQZqMpFkfxaSJFPt2XOW57S16eYZtjaN8pyc2bB9vFufldXpLY8fObb9tSa3qNXZ5Oll2cs8NyzNnP6gAXOn2d/7fE4fW8iBayaHvj7D27UO5lrrB5M8fIv2NyZ54zbn/FSSn9qtTAAAAAAAnDu7/Uv+AAAAAAC4SOlgBgAAAACgER3MAAAAAAA0Umqte52hsVarVYedPBwAAAAAgOGUUk7VWluD7UYwAwAAAADQyL69DgAAAADQs3RkJpnY3L48unF7LslKxjobq+PJfDKyuPm8iSSH2lNr28N+E/qCy3NyJkdHN96niZHFZDILG9qm2rPN6tNvYr02c92mldXp9f3zycEt6pIk+7OQqfbshrbG9VkdX7tfEwcXO3n6nav6NHV8NHnW0fVaHmpP3fbznFzPc/TAdOPn1e/g4vpnzMwH54cRzAAAAAAANKKDGQAAAACARnQwAwAAAADQSKm17nWGxlqtVjWXCgAAAADA7iqlnKq1tgbbjWAGAAAAAKARHcwAAAAAADSyb68DAAAAAPQsHZnprEystx0f3XjMyup0Mt9ZH1ncfI3JLGx57an2bIadavNCzbN8eOfj5nrZMrZ55+p4Dh7b3HyoPdW8Plvp1mx5dItMq+Prx3Vrd7CvdvuzcF7qs5anZ3U8md+YpadRfU6u16e/Dts9l14tBg2+VxPp1OjANZO37Tx9z+vEVhlO4+DR8Q3bvfemx9S654cRzAAAAAAANKKDGQAAAACARnQwAwAAAADQSKm17nWGxlqtVjWXCgAAAADA7iqlnKq1tgbbjWAGAAAAAKCRfXsdAAAAAKDnhqWZzG3RvpKxjQ2r41tfYL6zGFncvGsyCzlwzeQFk+e69lSG/Wb20smZJMny6Nb75/rWV9rTG+49MXDs/iysrU+1Z5PkrPLMpVuXXi3m14/r//knBu7df/9B57I+c93lyup0Mr85U89O2YbOc2Rmc2P3ZifePH3a869rT+24/7ae54al9Twn2qe//04ODnzGDjX4fNGMEcwAAAAAADSigxkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGim11r3O0Fir1aom6wYAAAAA2F2llFO11tZguxHMAAAAAAA0sm+vAwAAAAD0LJ2cyfHR9e2VjHVWVseTJCPH1vdNJNmfhTO+9lR7NsN+E/qCy3NkJscPJyur08n8evvI4sbjrmtPDXXdniZ5NpnoLJb76jaXvtr1tMczsrh2+Jr+Gh64ZrJxnsE6nYsaNarPRHL0wMbndTbaR9dzN8mzfDg5sXpu8owsbqzjsHmuXeo8r5WMZeTo+NkH6ppIcqg9NXQemjGCGQAAAACARnQwAwAAAADQiA5mAAAAAAAaKbXWvc7QWKvVquZSAQAAAADYXaWUU7XW1mC7EcwAAAAAADSigxkAAAAAgEb27XUAAAAAgJ6lIzPJRLI8msz1ta9kLFkdX2+YX18dWUwmuuuH2lM7Xn/YqTYv1DzDOD6arKxOb8jYM7KYXNeX8VzkWR7tLOe62ysZS9rrtRpZ7CwnkuzPwlr7VHt20/WHznNyZqjjk259BjL2TC8ubMjVqD5dy4fP7JwT7ekt29tHN79LF0WeId/nJDn6wa0z9fSymVr3/DCCGQAAAACARnQwAwAAAADQiA5mAAAAAAAaKbXWvc7QWKvVquZSAQAAAADYXaWUU7XW1mC7EcwAAAAAADSyb68DAAAAAPQsnZxJkiyPdrbntjhmJWPrG6vjyXwysrjzdSeSHGpPZdhvQl9weY7MbLpQL9uZmEs3b3s8Bwcy7s9CDlwz2SzPRGcxmGWuu1zJWKc2fUaOrZ225lB7asP2hVSfps9r+fDm9hMZy/TROwx1rX5T7dkkzepzoeUZykRy9IPTSZKDi5vfl0FmPjg/jGAGAAAAAKARHcwAAAAAADSigxkAAAAAgEZ0MAMAAAAA0Eipte51hsZarVY1WTcAAAAAwO4qpZyqtbYG241gBgAAAACgkX17HQAAAACg54almbX1uW2OWVmdPuPrHTyWHGpPrW0P+03oCy3P0pFunonk+GiykrEdjx85Op4kmczCjsdNtWeb5Tm5Xp/l0c5ybovjNuRcHV9fn19fHVlMJrrr+7OQqfZss/p0a7PlvQetjmfkWGd1pxo1rU//+zNo7jTnrqxOb6hPkkwvLqxlOe952pvf8/bRqQ3bw+a5dps82z2zkaPjp32Xe5q8PzRjBDMAAAAAAI3oYAYAAAAAoBEdzAAAAAAANFJqrXudobFWq1XNpQIAAAAAsLtKKadqra3BdiOYAQAAAABoRAczAAAAAACN7NvrAAAAAAA9SydnkiTLo53tuW77SsY2H7w6vr4+31mMLCaTWVhrnmrPbjhl2Kk2L7Q8NyzN5MRW9x5Wez3r9OLCWq6h63NkZn1jorPo1apnrrvcsmZJp2599Uo6NZtqzzaqz9xO9zpT57I+E6c/7vjo9vu+4+jjtmxvUp/e+3yh5Llhaes8JzK28fO0k/mNmyOLyXXtqSTDPy+aMYIZAAAAAIBGdDADAAAAANCIDmYAAAAAABoptda9ztBYq9Wq5lIBAAAAANhdpZRTtdbWYPuujmAupdxUSvmbUso7Synv6LZ9VSnlzaWUD3SXd+u2l1LKK0spN5ZS3lVKeeRuZgMAAAAA4OzsOw/3eFKt9eN92z+c5E9qrdeWUn64u/1DSZ6R5AHdP49NcrS7BAAAAL5MLB2ZyfLhZK6vbSVjyer4pmNHjm0+f6JvfX8WkiRT7dm1tmG/CX2h5bl2aaZz/2TLDEmS+dNfZ2Qxua49tam9aX36zXWXG3Juk2lksbOcSHLoHOQ5bX3OoDa9XOeiPjM3Lp3xsdPHFnbc3//eXDR5lt4w1PFpb/POdw0+NzMfnB97MQfzs5O8prv+miTP6Wt/be24Icm/KqV87R7kAwAAAADgDOx2B3NN0i6lnCqlfHe37WtqrR/trv9jkq/pro8k6f/PKB/utm1QSvnuUso7SinvuPnmm3crNwAAAAAAp7HbU2R8Q611uZTy1UneXEp5X//OWmstpQz1WwZrra9O8uqk80v+zl1UAAAAAACGsasjmGuty93lx5K8McljkvxTb+qL7vJj3cOXkxzoO/3e3TYAAAAAAC5ApdbdGQRcSvnKJJfUWj/TXX9zktkkT0nyib5f8vdVtdaXl1KemeSlSb45nV/u98pa62N2uker1aom6wYAAAAA2F2llFO11tZg+25OkfE1Sd5YSund59drrX9YSvnLJG8opRxO8qEk39o9/g/S6Vy+Mcnnk7xoF7MBAAAAAHCWdq2Dudb6wSQP36L9E+mMYh5sr0leslt5AAAAgAvf0pGZzsrEmZ9zfDS5y9HpTafsz0Km2rMb2ob9JnSTPEdHxzJydHx382xj+XByYnU6md96/8ji5raJJIfaU2efZ+AHXh7tLE9kbL1xdXxDtv48E+nUKMlanc51fY4eHkva49vuH6xPf20a5Tm5nuf46Jmft7I6veP+kWPJde2pxnmGyXI+8iTJ0Q/ufI/TObjFu335NQc2N3LO7eoczAAAAAAAXLx0MAMAAAAA0IgOZgAAAAAAGimdqY9vm1qtVh12bhcAAAAAAIZTSjlVa20NthvBDAAAAABAIzqYAQAAAABoZN9eBwAAAADoWToyk0ysby+PJnMDx6xkbPsLrI4n8+ubI4udy+3PQqbasxl2qs0LMk+f5cOdPCsZ69wrycixzr7efbYy1Z7dsr1pnuXD620ntqrH6vharn59pV3L2p/tbOqzoTYDGXr33e36zCy9Ye3ew+ivVX+NDrWnLqo8NyzNbHp/hzKfTC9u/QyT5MA1k8Nfk6EZwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjZRa615naKzVatVh53YBAAAAAGA4pZRTtdbWYLsRzAAAAAAANLJvrwMAAAAA9CwdmdmwvXy4s5xLspKxzsbqeDKfjCxuPn+iu9yfhbW2qfbs2vqw34S+EPMcP3z641Yy1snVs02+/pz7s5AD10wOnWfth+xaHu0s53pZVqc3HtCXpXffrUy1Z3evPjtkGtTL2DTP8uH1WuyYKWMZOTo+WM41W71DF0OenWz3LAefX/vqqS2PM/PB+WEEMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGSq11rzM01mq1qsm6AQAAAAB2VynlVK21NdhuBDMAAAAAAI3s2+sAAAAAAD1LR2a23zmRHB9NVjKWrI4n853mkcXOcjIL25461Z5Nkgz7TegzztMeX2vekzwTWzcvjyZz3fWVjGXk6HrOySys5egZOs/JjXmWR5MTGetsrI5n5NjG43eqSU9/pnNSn21qk3SeX8/K6vRa3okk+89BfW5Y2vp5zZ3mvJX29Np7NKj34xxqT93m8ywdmcny4a3vv9J7j5IN7+12eu/W2bw/NGMEMwAAAAAAjehgBgAAAACgER3MAAAAAAA0Umqte52hsVarVc2lAgAAAACwu0opp2qtrcF2I5gBAAAAAGhEBzMAAAAAAI3s2+sAAAAAAD1LR2Y2Nkyc+blHD0zn4LFkfxYy1Z7d8phhp9q80PLcsDRz+oOSnMhYZ6U9fsbXbh+dGr4+J2eyPLq+PZdkpXfvDRc/sxwji8l17am17d2oT5PaJA3rM/D+LB/uZlid3njg/OmvNbK4cfu69m0/z5m+z4NObPWOJRue6chi8pJrDjS6PsMxghkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGim11r3O0Fir1arDzu0CAAAAAMBwSimnaq2twXYjmAEAAAAAaGTfXgcAAAAA6Fk6MtNZmegsjo+u71vJ2MaDV8eT+fXNkcXN1+teJvuzkKn2bIb9JvQFl+fkzBkfuyHr6nRGjm3MkCSH2lMbzmlcn56JjZv9GZJuzVbH1xvmk4N9derVpXGeC7g+y4eTE4PvzICRo+Ob2ia2OK6Xq2me5cOd7T3PM8TzOhMn3jy9lu9Qe2roPDRjBDMAAAAAAI3oYAYAAAAAoBEdzAAAAAAANKKDGQAAAACARkqtda8zNNZqtarJugEAAAAAdlcp5VSttTXYbgQzAAAAAACN7NvrAAAAAAA9S0dmOisTyfLoevtckpWMdTba4xlZ3Pr8ib71/VnIVHt2w/5hvwm9XZ4TvSx7lWeHm/Xn7DeXvhqujmfkWGd1si9XozwD9127z+p4kqzdZ9BkFjZsD9amcZ6tDFGbkaPjG/adTX1uWNqYZ26b41b636ckaY9nenFhy2N7ptqzu5JnU5ZdzLN0cmOebT9jQxo5Op7JLOTANZONr8GZM4IZAAAAAIBGdDADAAAAANCIDmYAAAAAABoptda9ztBYq9Wqw87tAgAAAADAcEopp2qtrcF2I5gBAAAAAGhEBzMAAAAAAI3s2+sAAAAAAD1LR2a23jGxvnp8NFnJWLI6npFjG3fvz8KG06basxu2h51q83R5lkeTuWzM0xf1/OXpy3R8NFlZnV5r7tVoO5NZWMs1dJ6TG/P06tGzkrH1jdXxzReYX18dWVzPk3Rqda6f1/HRbq6++mR+/d7bua49laT581o+PNRpG5zIWA4eHV97l/rfoYslT8/xwwPvzFYG3qORY+vPZ5Cpdc8PI5gBAAAAAGhEBzMAAAAAAI3oYAYAAAAAoJFSa93rDI21Wq1qLhUAAAAAgN1VSjlVa20NthvBDAAAAABAI/v2OgAAAABAz9KRmY0NExs3l0eTue76yup0Mp+MLK4fuj8LmWrPbnv9Yb8JfcHlOdnJc/SD00Odt5P20anmeY7MbKjJ0dGxzsrqeDK/+fhebZL1+iTZtkZ7XZ/+2jTKc2Qmn/7n6bWf82xsVaPbfJ7u81oeTU60z/0zM/PB+WEEMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGSq11rzM01mq1qsm6AQAAAAB2VynlVK21Ndi+6yOYSymXllIWSim/191+cinlr0op7y6lvKaUsq/bXkopryyl3FhKeVcp5ZG7nQ0AAAAAgOb2nYd7fH+S9ya5aynlkiSvSfKUWuv7SymzSV6Y5FeTPCPJA7p/HpvkaHcJAAAAfJlYOjKz/c6JrZuPjyYrq9M5eKyzvT8LWx431Z7NsN+EviDzTKzfp99KxpLV8WQ+Obi43r7d/c91np7l0eRExtYb2uMZWdx4zMQZ5DrbPMujyVy3faWb5+DR8bVjz7Qu5zLPmZjLet6ekW7uyW7mqfZsktzm88wsveGMjjt4dPy8PC+a2dURzKWUeyd5ZpJf6TbdPck/11rf391+c5LndtefneS1teOGJP+qlPK1u5kPAAAAAIDmdnuKjFckeXmSW7vbH0+yr5TSm6vj3yY50F0fSbLUd+6Hu20blFK+u5TyjlLKO26++eZdCQ0AAAAAwOntWgdzKeVZST5Waz3Va6ud3yh4dZL/Xkp5e5LPJLllmOvWWl9da23VWluXXXbZOc0MAAAAAMCZK50+3124cCk/neQFSb6U5A5J7prkd2qtz+875qlJ/l2t9VtLKf8zyVyt9XXdfX+X5Im11o9ud49Wq1XNpQIAAAAAsLtKKadqra3B9l0bwVxr/ZFa671rrfdNZ9TyW2qtzy+lfHU30O2T/FCSV3VPOZHkO0vH45J8eqfOZQAAAAAA9ta+Pbjny7rTZ1yS5Git9S3d9j9I8s1Jbkzy+SQv2oNsAAAAAACcoV2bIuN8MEUGAAAAXFyWjsx0Via23r88msz1ba9kLFkd72zMb33OwcXOcn8WcuCaydt2npMzG7aPjyYrq9M73m8n04sLa+tT7dkM28+ydGRm29r0HB0dS9rjm9pHFpPJLGw+oZslyfB5zmF9+mvTyzRsnmuXOnlWMrbeuLq5Fj3tq6eGuv6u5plP2kd3N88N3TwnVqfP+vkMavK82Nl5nyIDAAAAAICLmw5mAAAAAAAa0cEMAAAAAEAj5mAGAAAAAGBH5mAGAAAAAOCc2rfXAQAAAAB6lo7MbL1jIlke7azOJVnJ2Pq+1fGMHNt0+JpD7am19WG/CX2bybPFjXr5euaSrLSnkyQji5tP25+FHLhmslmeia3vl/TVZnU8md94zMjixtr07M9Cptqzu1afwaxJciJjSXt8Q7a+U86uPv0XyvYZ5s7wuivt6UwvXgR5Tq7n2er+PaeOTg913SSZbPC8aMYIZgAAAAAAGtHBDAAAAABAIzqYAQAAAABoRAczAAAAAACNlFrrXmdorNVq1WEnewcAAAAAYDillFO11tZguxHMAAAAAAA0sm+vAwAAAAD0LB2ZSSaS5dFkLslKxjbsHzk6fsbXmkiyPwuZas+utQ37TegLLc/M0hu23rG6Q475ZGTxzK7/kmsODJVn6eTM2nqvRj0rGducayDLRHc5WJeeC6k+17Wnhs5zw9JMTgy8M1vaKd+gbt6LIc/SkZkcPXxmeUaOnf6w69pTG7bNfHB+GMEMAAAAAEAjOpgBAAAAAGhEBzMAAAAAAI2UWuteZ2is1WpVc6kAAAAAAOyuUsqpWmtrsN0IZgAAAAAAGtHBDAAAAABAI/v2OgAAAABAz9KRmWQiWR5N5pKsZKyzY3V888HzncX04sK215tqz27YHnaqzWHzjCwmk9nlPF3LhwcytbfI1GdkMZkYaNufhQ2Zhs1zw9J6nrm+9rVMg3p1m986X9KpXy9T0/r0arMpy5A1Otv69N6fJDk6OrbjsVkd37Iu22kfnTqr9/nEds/oPOaZ+dWlJMnBxaFOS9J5NjuZas8OnYdmjGAGAAAAAKARHcwAAAAAADSigxkAAAAAgEZKrXWvMzTWarWquVQAAAAAAHZXKeVUrbU12G4EMwAAAAAAjezb6wAAAAAAPUtHZtbWlw93lnPd7ZWMbT5hdTyZ76yOLHaWE1tcd38WkiQHrpm8aPL055pLsrI6vXHH/ObzpxcXMtWe3fb6w35TfKs8ayaS5dH1zbl0a7Y6niQZOdapzf5sn+lc5jldnQ4u7pzlbPIsH05OZCxpj2977MhiMtl9L87EVHu2cZ7jh7t12OId6enVYzfzXLu08XmtPZsdcm3n4OLmtsuvOTD8hRiaEcwAAAAAADSigxkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGim11r3O0Fir1arDTh4OAAAAAMBwSimnaq2twXYjmAEAAAAAaGTfXgcAAAAA6Fk6MpNMbGxbHl1fn+trX8lYZ2V1vLOcX983srjxGhNJ9mchB66ZvM3nOXp4bOudq+OZPraQqfbsUNfsN+w3xZeOzKxvdOu0PLpel5WMdeox36lBfyn3Z2Hb6/Z+hiZ5jh/uexb92uOZXlzYcP1hNarPxPb7j/e9SysZS9rjOdh9V3aqT9L5GXY1z+p0Mp9dzXPt0szWz2pYq+MZOdZZncz6MzbzwflhBDMAAAAAAI3oYAYAAAAAoBEdzAAAAAAANFJqrXudobFWq1XNpQIAAAAAsLtKKadqra3BdiOYAQAAAABoRAczAAAAAACN7NvrAAAAAAA9S0dmNjZMJMdHO6srGeusrI5vPnF+fXVkMZlIsj8La21T7dkkybBTbV7weZIsH975nLkt2lZWpzvZjm3MduCayeHzTCTLoxvvs5KxTl3mOz9/0rlPsnUdtnNW9enm2sncwPZg7v7aTLVnh89zspNnqxynjk6v1aTnUHtqqOvf1vPcsLT5fd7J3Bket7I6nfbVU0PnoRkjmAEAAAAAaEQHMwAAAAAAjehgBgAAAACgkVJr3esMjbVarWouFQAAAACA3VVKOVVrbQ22G8EMAAAAAEAj+/Y6AAAAAEDPDUszm9rmusuVjO188up4Mp+MLCbXtae2PGTYb0JfaHmWjsxk+fD2++cGc62Ob3vsyLHNuZrkyURnfXl0Y44MZunlmd8iy+L6+mQWMtWebZ4nOSc1Olf1Ob5DlkFn8k61r17PdJvPc3Lz52snRz84fcbHHlxMLr/mwFDXpxkjmAEAAAAAaEQHMwAAAAAAjehgBgAAAACgER3MAAAAAAA0Umqte52hsVarVYedPBwAAAAAgOGUUk7VWluD7UYwAwAAAADQyL69DgAAAADQs3RkJkmyfHhj+1x3uZKxZHV8fcf86a95cDHZn4VMtWcz7DehL7g8J2c2bB89ML22PnIsmczC2vZUe3aoaydplGd5tLM+N7BvJWPrG/016umr1chiZ9nL38t+LuuT+WR6cWHD/mFr1OT9OX44ucvR9RwTQ11ha4faUxdHnoHndXw0WWlPb3P0cNpHp4bOQzNGMAMAAAAA0IgOZgAAAAAAGtHBDAAAAABAI6XWutcZGmu1WtVcKgAAAAAAu6uUcqrW2hps3/URzKWUS0spC6WU3+tuP6WU8lellHeWUt5aShnttt++lPIbpZQbSyl/UUq5725nAwAAAACguX3n4R7fn+S9Se7a3T6a5Nm11veWUr4nyY8lOZTkcJL/W2sdLaVcneRnknzbecgHAAAAXCCWjswkE53146PJXY5O9zazPwuNrjnVnl1bH/ab0BdanhuWZpIkc0lWMrb9gavjG7fnk4OLndVD7altT2tUnyTLh7N9rtXxZH7zuSOLa6XdpFfbA9dMDpWnV58kObFTfXq5euY35tmfhQ3PqWfY+lzbl2cnK6vTmxvnk+nFTh22ynK+84wcSyZzbvMsnZzJ8ugZvM+DBt/vft137eBicvk1B4bKQzO7OoK5lHLvJM9M8it9zTXrnc37k3yku/7sJK/prv9WkqeUUspu5gMAAAAAoLndHsH8iiQvT3KXvrZ/l+QPSilfSLKS5HHd9pEkS0lSa/1SKeXTSe6e5OO7nBEAAAAAgAZ2bQRzKeVZST5Waz01sOs/JfnmWuu9k1yX5OeHvO53l1LeUUp5x80333yO0gIAAAAAMKzdnCLjqiSTpZSbkrw+yZNLKb+f5OG11r/oHvMbSb6+u76c5ECSlFL2pTN9xicGL1prfXWttVVrbV122WW7GB8AAAAAgJ2UWuvu36SUJyb5wSTPSfKPSb6+1vr+UsrhdEYzP7eU8pIkV9RaX9z9JX/fUmv91p2u22q16rCThwMAAAAAMJxSyqlaa2uwfbfnYN6gO7fyv0/y26WUW5P83yTf1d39q0n+VynlxiSfTHL1+cwGAAAAAMBwzksHc611Lslcd/2NSd64xTFfTPK885EHAAAAuDAtHZk584MnkuOjyUrGktXxteaRYxsPm8xCkmSqPZthvwl9weU5OZPl0W4nS7r36um756Behokk+7v372Xo17Q+y4c3ts/1rW/IuFXO+WRkcT3fofZU8zw71Were/cZOda5f7Jeo3NVn7ULd/Vn3MnK6vT6xnxnMb24/vwOXDN5weRp8j5fu9TJs+k59ezwvA4e2/gub2XY+tDMbs7BDAAAAADARUwHMwAAAAAAjehgBgAAAACgER3MAAAAAAA0Umqte52hsVarVYedPBwAAAAAgOGUUk7VWluD7UYwAwAAAADQyL69DgAAAADQc8PSzKa2uSQrGTv9yavjG7fn11dHFpPr2lMZ9pvQF1qepSPdPBPJ8ugZZlkdz8ixZKKvaX8WNh021Z5tnqebKenk6pnrO3Yt5+r4hlocXNx4zV62s8ozsZ7jxGnqM3J0fENt+jP0a5Ln2i3enzO1sjqdzHfelX6TWchUezZJbvN5bliaOfPP0xk6eHQ8h9pTjfLQjBHMAAAAAAA0ooMZAAAAAIBGdDADAAAAANBIqbXudYbGWq1WNZcKAAAAAMDuKqWcqrW2BtuNYAYAAAAAoJF9ex0AAAAAoGfpyMzOB0x0FsdHk5X2dEYWdz50fxaSJFPt2STJsN+EPpM8y6PJXM5TnpMzaz/7oMF7Tw7c60w0qk/3mSyPJicy1tlYHU/m1zP1/+yDdso3bJ5rl2ZOW5vJvhzD1KZJnqWTnffn6OjY1gesjneW8ztf5+Bicqg9dfHl6X6+jh/ubG/17HbSe66TWdjyWZr54PwwghkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGtHBDAAAAABAI6XWutcZGmu1WtVk3QAAAAAAu6uUcqrW2hpsN4IZAAAAAIBG9u11AAAAAICepSMzycTm9uXR9fW5gX0rGUtWx5P5zvb04sKG/VPt2bX1Yb8JPWyelYx1VnYpzw1LMxu257Y+bEcrq9OdlflkZLHz4x1qTzXK06vPTs8nGahLz/zGY0YWk+u6OXrOpj5b5TidwXdpZDGZzMLaM2ua50TGkvb4zgd377eTiST703mfptqzzfO0p8/o+NPlmcz6u90kz9KRTp7lw+ttcwPHrKxOb3pXttKf9bqG7zPNGMEMAAAAAEAjOpgBAAAAAGhEBzMAAAAAAI3oYAYAAAAAoJFSa93rDI21Wq1qsm4AAAAAgN1VSjlVa20NthvBDAAAAABAI/v2OgAAAABAz9KRmfWNic5ieXTjMXPd5crqdEaObTx8fxY2XXOqPbu2Puw3oS+0PDcsdfKcaE8Pdd5O2kenGudZq8/Eel3mkqxkLGmPn/F1Di5urFWvRo3rszqdzA916rbOSX2SLB/evH8unfdmOwf73qdD7alN+891nhM7ZNmVPCf78oyeu/e698zMfHB+GMEMAAAAAEAjOpgBAAAAAGhEBzMAAAAAAI2UWuteZ2is1WpVc6kAAAAAAOyuUsqpWmtrsN0IZgAAAAAAGtm31wEAAAAAepaOzCQTG9uWR5O5JCsZ6zSsjifz6/tHFjceP3B69mchU+3ZJMmw34Q+2zyDWc5JnoGLL4+uN23I1a89vqlpZHFjvkPtqbPLM2iHfCNHx7esTXIO67NNhiQ5MVijLepzcOC9alqf44c3tq2sTm9/wnwyvbhwRteeas/e9vOc7DyvweeznbnuciVjm57Z4Pu8Pws5cM3kUHloxghmAAAAAAAa0cEMAAAAAEAjOpgBAAAAAGhEBzMAAAAAAI2UWuteZ2is1WrVYScPBwAAAABgOKWUU7XW1mC7EcwAAAAAADSyb68DAAAAAPQsHZnprEyc+TnLo8mJ1elkPjm4mOzPwob9U+3ZtfVhvwl9oeW5YWlmy/a5JCsZS1bHkyQHj21/jUPtqW33NarPROdnHszTbyVj6xvdjEmS+WRksbM6WOJD7aldq0/SrEaN358zNVCET795etMhvfdpqj17m8+z3fNKNr9DSbKyuvn+W+k928uvOTBUHpoxghkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGtHBDAAAAABAI6XWutcZGmu1WnXYycMBAAAAABhOKeVUrbU12G4EMwAAAAAAjezb6wAAAAAAPUtHZrbdt3x4c9uJjCWr42vbI8c27p/MQqbas2vbw34T+kLLc+3S5jwrGdv64L4ca+aT6cWFLQ+fas8OX5+TM1keTeZ2ytOfY37zNUYWk4nu+v7zWZ/BbN18Bxc7Ofr1Mp3L92dH3YIsjyZ3Pjq9YVd/jc53nk+/eXrTrrPJs9XzOhNrz3SrdzxZe88PXDPZ6PoMxwhmAAAAAAAa0cEMAAAAAEAjOpgBAAAAAGik1Fr3OkNjrVarDju3CwAAAAAAwymlnKq1tgbbjWAGAAAAAKCRfXsdAAAAAKBn6eRMlkc763OnOXYlY8nqeDLf2T64mBxqT+14zrDfhD6bPNOLC5lqz57bPEdmsnx4Y9uJjHVW2uMb2g8unvl1e3VrkmeDic6iv2Yr2+QbWVw7fFOOnnNWn3NQm0Z5Ts5su+/4aLKyOp2Dxza278/CGV//wDWTt+k8NyzNdN6R9vRQ5w3a7nlefs2Bs7ouZ8YIZgAAAAAAGtHBDAAAAABAIzqYAQAAAABoRAczAAAAAACNlFrrXmdorNVq1WEnVwcAAAAAYDillFO11tZg+66PYC6lXFpKWSil/F53+/8rpbyz++cjpZQ3ddtLKeWVpZQbSynvKqU8crezAQAAAADQ3L7zcI/vT/LeJHdNklrr43s7Sim/neR3u5vPSPKA7p/HJjnaXQIAAABfJpaOzGT5cGd9bmDfSsY2n7A6niQZOZZMbHG9/VnIVHt2bXvYb0JfcHlOzqytHz0wvWHfdvc8U/uzkAPXTA6X58jMppseH9183ErG1mqzyXxncXBxY5ap9uw5rU9ydjVqUp8blma2bD+x1bvT096mToOHHZ0auj4XWp6lI33P6/kbn9fBY0NdatNnKxn+80UzuzqCuZRy7yTPTPIrW+y7a5InJ3lTt+nZSV5bO25I8q9KKV+7m/kAAAAAAGhut6fIeEWSlye5dYt9z0nyJ7XWle72SJKlvv0f7rZtUEr57lLKO0op77j55pvPbVoAAAAAAM7YrnUwl1KeleRjtdZT2xzy7UleN+x1a62vrrW2aq2tyy677KwyAgAAAADQ3G6OYL4qyWQp5aYkr0/y5FLKryVJKeUeSR6T5Pf7jl9OcqBv+97dNgAAAAAALkCl1rr7NynliUl+sNb6rO72i5OM11pf2HfMM5O8NMk3p/PL/V5Za33MTtdttVrVZN0AAAAAALurlHKq1toabN+3F2GSXJ3k2oG2P0inc/nGJJ9P8qLzHQoAAAAAgDN3XjqYa61zSeb6tp+4xTE1yUvORx4AAADgwrR0ZCaZ6Kwvj3Y6E1ZWp5P5Mzv/4GKyPwtb7ptqz2bYb0JfyHmS5PhoN0/PQK6Dixu3D7Wndrx+ozxJMtHJ0m8lY52V1fGNO+Y359qqRrtSn+79ewZzJDvX6GzqM4yjB6Y3N3Zz9zIfak9dHHlO97z67nUm2kfXn5+ZD86P3ZyDGQAAAACAi5gOZgAAAAAAGtHBDAAAAABAI6Uz9fFtU6vVquZSAQAAAADYXaWUU7XW1mC7EcwAAAAAADSyb68DAAAAAPQsHZlJJpLjo53tlYxtfeDqeJJk5Nh600SS/VnY8vCp9mySZNhvQl9oea5dmjmj49ZydnP1TB9bWLv3VhrVp2eis1geTeYGc/SyzHdWRxY7y8mc2zxnU5/T1aZJnrX6TGx/TO/d2pRrfvtzDi523q0D10ye0zz9z+5M84wsdi53qD01dH1uWJrZcL8mNrxj7c7zbB+dSjL886IZI5gBAAAAAGhEBzMAAAAAAI3oYAYAAAAAoBEdzAAAAAAANFJqrXudobFWq1VN1g0AAAAAsLtKKadqra3BdiOYAQAAAABoZN9eBwAAAADoWTo5s+P+46PJyup0po8tnNH1ptqzG7aH/Sb0BZfnyM55kiQTyfJoMtfXtLI6vb4x31mMLG487br21NnnmVhfPT7avXfGktXxZL5zz4kk+7OxXoN16dmr+mxVm7PNs3x48/4TGRvqegePjudQN8tFkWfg89V7Z5JkpT2dYQw+syR5yTUHhroGzRjBDAAAAABAIzqYAQAAAABoRAczAAAAAACN6GAGAAAAAKCRUmvd6wyNtVqtOuzk4QAAAAAADKeUcqrW2hpsN4IZAAAAAIBG9u11AAAAAICepZMzWR5N5rrbK6vTGTmWTGZh23Om2rNnfP1hvwm9dHImx0fXt/c8z5GZHfcfP7z9vpWMdVZWx3PwWLI/C5uynlWeic5iuVuvuf57du+b+c7qyOLa4Rvs76vrVHv23NZnIhueZb/+nAePjq+tH2pPbTiuyfuTrNekZ27w/t336kz06naoPXWbz3Pt0s7v84ZM/e/SDkaOjq9luvyaA0PloRkjmAEAAAAAaEQHMwAAAAAAjehgBgAAAACgkVJr3esMjbVarTrs3C4AAAAAAAynlHKq1toabDeCGQAAAACARvbtdQAAAACAnqUjM52ViWR5tLM6l2QlY52N1fFkfvN5BxeTQ+2p015/2G9CnzZPe3zL83Y9zw6WDycnevm2MXK0k3syC5lqz577PBPdLFvVrKc9npHFtUPX7O/LtBv1SZKjh8d23H+u6nPD0vZ55oa6UrLSns7Bxc567926reeZWXrDhs/QyOKZnbfVO7OVA9dMDpWHZoxgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGdDADAAAAANBIqbXudYbGWq1WHXbycAAAAAAAhlNKOVVrbQ22G8EMAAAAAEAj+/Y6AAAAAEDPDUszSZK5JCsZW9/RHm90vZHF5Lr21Nr2sN+E7uU50Z/lAshzpua6y7Varm6f++Cx5PJrDgx1/aUj3TwTyfLozhm2yzFybO0SSZL9WchUezbJ+avPdtn6NanPtUszG9/jJrrv2sHFTm16ptqzQ9fnQsvTe3+WDw+8qzu8p9sZOdZ5hw6dxeeLZoxgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGdDADAAAAANBIqbXudYbGWq1WNVk3AAAAAMDuKqWcqrW2BtuNYAYAAAAAoJF9ex0AAAAAoGfp5MyW7cujneXcQPtKxjY2rI5vPnk+GVlMJrOQA9dMXjR5lkd3uP/qeDK/3j69uJCp9uxprz/sN8WXjswkE5vbe9nW8rQ316FXg0H9OYfOM/C8jo+ur29Xm2T36nPD0kxOrE5vut8wRha3LHGS5PJrDgx1rWuXZrKyS3n2n8PPV0//8zudlYytf966n7GXDFkfmjGCGQAAAACARnQwAwAAAADQiA5mAAAAAAAaKbXWvc7QWKvVqsPOfQMAAAAAwHBKKadqra3BdiOYAQAAAABoZN9eBwAAAADoWToys74xsb66PJrMdddXMrb5xNXxznK+sxhZ3HiZ/VlIkhy4ZnJP8/QucU7yNDWxnv9ZR6fXsky1ZzPsN8WHztO9d7+5dGvYHs/BxZzfPDtknMu5q8/Rw2NJe3yoc/vf4S0i5lB76qLIs3x4c/vcUFfpWMlYDh4dz6H21FqbmQ/ODyOYAQAAAABoRAczAAAAAACN6GAGAAAAAKARHcwAAAAAADRSaq17naGxVqtVTdYNAAAAALC7Simnaq2twXYjmAEAAAAAaGTfXgcAAAAA6Fk6OZPl0c763Bb7V1anOyvzG9unFxcy1Z497fWH/Sb0BZfnyMyW7cuHN+dbWZ1O5pODi8n+LGw6Z6t8jfJMZMsarWQsWR3vbAzUZ2Sxs5zobh9qT215/bOuTzfb3MBxvdoknWc1aLtndy6f14mMrTf06jRofuvmJGkfnboo8/Te5ZX+PKcxcnQ8123xDpn54PwwghkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGim11r3O0Fir1armUgEAAAAA2F2llFO11tZguxHMAAAAAAA0susdzKWUS0spC6WU3+tul1LKT5VS3l9KeW8p5fv62l9ZSrmxlPKuUsojdzsbAAAAAADN7TsP9/j+JO9Nctfu9qEkB5I8uNZ6aynlq7vtz0jygO6fxyY52l0CAAAAXyaWjsx0ViY271seTea66ysZS1bHOxvz68ccXNx4zv4sJEmm2rNJkmGn2lw6MrNllj3NcwaOH964vZKxjBwdz0Q3Q+/+g84qT1+d+muzIcfqdGdlvlObQ+2pHa+/K/WZSI6Pbs518NjOtTlXeU788/RQ1xh0XV/Nbut5blia2fI96beSsTO61sjR8UwOPD9T654fuzqCuZRy7yTPTPIrfc3TSWZrrbcmSa31Y932Zyd5be24Icm/KqV87W7mAwAAAACgud2eIuMVSV6e5Na+tn+d5NtKKe8opfyfUsoDuu0jSZb6jvtwt22DUsp3d899x80337xLsQEAAAAAOJ1d62AupTwrycdqracGdt0+yRe7v3Hwl5McG+a6tdZX11pbtdbWZZdddo7SAgAAAAAwrFJr3Z0Ll/LTSV6Q5EtJ7pDOHMy/k6SV5Bm11r8vpZQkn6q17i+l/M8kc7XW13XP/7skT6y1fnS7e7RarWouFQAAAACA3VVKOdUdNLzBro1grrX+SK313rXW+ya5Oslbaq3PT/KmJE/qHjaR5P3d9RNJvrN0PC7Jp3fqXAYAAAAAYG/t24N7XpvkeCnlPyX5bJJ/123/gyTfnOTGJJ9P8qI9yAYAAADsoaWTMzk+urFtZXX6zC8wn4wsrm9OdJf7s5Cp9myG/Sb0YJ6VjCWr443yTPQ1N81z7dLMeo6tnEG2kS0mK72uPZUkjeqzPLq5fS47ZEw25ezP1MvSJM+1SzND3Xcr57I+vee1kzN6v+eT9tGpTc0XQ54dn1cy3Oeta+RY55mZ+eD8OC8dzLXWuXT+bkmt9VNJnrnFMTXJS85HHgAAAAAAzt6uTZEBAAAAAMDFTQczAAAAAACN6GAGAAAAAKCR0pn6+Lap1WpVk3UDAAAAAOyuUsqpWmtrsN0IZgAAAAAAGtm31wEAAAAAepZOzmR5NJlLspKxZHW8s2O+s5heXNjx/Kn27I77h/0m9IY8q9PrO/Yqz5GZZKKzvjzaWW7KtpP5zU0ji8l17anmeZK1TP025MvY+o7V8YwcWz9tfzo13KpWZ1OfTRka1qh9dOrs8iQ5fvjMjl9pdzKOLG4uaa9OyXqtmtbn+OgZZMlY0u58/g4ubt5/zvJs4/jh7Z/ZwWNbn3OoPbVh28wH54cRzAAAAAAANKKDGQAAAACARnQwAwAAAADQSKm17nWGxlqtVjWXCgAAAADA7iqlnKq1tgbbjWAGAAAAAKARHcwAAAAAADSyb68DAAAAAPQsHZlJkiwf3rxvLslKxtYbVsfXVkeOrTdPdJeH2lObrjHsVJuN8swnI4u7k+fapU6etfu2O/fsv98wJrOwYfvANZNDnb90cmbD9vLoQF36nlHmN547WKP9fVmm2rNJ9rY+g7WZas8OneeGpZnMDXH8hvdpwMjR8bU6nW19zjbLucxzuvv07jWs69pTQ+ehGSOYAQAAAABoRAczAAAAAACN6GAGAAAAAKCRUmvd6wyNtVqtai4VAAAAAIDdVUo5VWttDbYbwQwAAAAAQCP79joAAAAAQM/SkZn1jYnOYnk0mRs4biVjnZXV8WR+62uNLK5f5lB7Kkky7Dehh8qzOt7Z2M08Jzt5jo8mK6vTG69/LJnMQqbas0Nds1/TPEmnLoPmusu155Ws16mnW6/pxc3Zz6o+/c+k62xrdDb16eU6Eyur05k+trDjMVPt2Ysuz+n0v/eny5MkB66ZHOr6NGMEMwAAAAAAjehgBgAAAACgER3MAAAAAAA0ooMZAAAAAIBGSq11rzM01mq16rCThwMAAAAAMJxSyqlaa2uw3QhmAAAAAAAa2bfXAQAAAAB6lo7MdFYmOovl0WRui+NWVqeT+c3tI4trpyZJ9mchU+3Zte1hvwl9IeY5frh7z4xtf+Dq+FqekcXOcjIL2x7ey3Q29enVZlOuviz9erm2ytY4z8mZHB/trG9bn4E856U+yfpzW53OwWNDXWaTQ+2piyLPzNIbTnvM9NE7NMqUJAeumWx8LmfOCGYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaKTUWvc6Q2OtVqsOO7cLAAAAAADDKaWcqrW2BtuNYAYAAAAAoBEdzAAAAAAANLJvrwMAAAAA9CydnMnyaDLX3V7J2MYDVsc3bs8nI4vJRJL9WUiSTLVnt73+sFNtXnB5jszk6IHpJJ379JvY4vhehp6dsjTNk4ms1WglY52azGdDxl62wTyny7Vb9Rk2x9nk2fLBJDk+evrzV1anc/DY1vsOtadu83lmfnVpqOOTzc91codnaWrd88MIZgAAAAAAGtHBDAAAAABAIzqYAQAAAABopNRa9zpDY61Wq5pLBQAAAABgd5VSTtVaW4PtRjADAAAAANDIvr0OAAAAANCzdGQmy4fXt+eSrGQsWR3vNMzvfP7I4ua2iSSH2lNJkmG/CX0h5jmtifXV5dHkRF/ekWMbdmd/FjLVnl3bPus8E5179ptLt2bJet165js16mU653m20r3Z8ugWzzMba3TWeU5uzNOrzVx3e0NddniXBt+jySwkSQ5cM3lR5Tk+us2BfflGjo6v3W8nU+3ZoZ8XzRjBDAAAAABAIzqYAQAAAABoRAczAAAAAACN6GAGAAAAAKCRUmvd6wyNtVqtarJuAAAAAIDdVUo5VWttDbYbwQwAAAAAQCP79joAAAAAQM/SkZn1jYlkebSzOpdkJWPJ6vjGE+Y7i5HFtVM22Z+FJMlUezbDfhP6gstzcmZTW3+m9HIlG7PNb77WwcWNeZLkwDWTw+U5MrPhh+xl6eVZy9KfZ75Tn4mBe/ebas8myTmtTy9TcvoabVWbpEF9tsizleXRLbL1rI5v+fymFxcuqDzto1NDP69fnFka6vgkmdzmnRnU5PNFM0YwAwAAAADQiA5mAAAAAAAa0cEMAAAAAEAjpda61xkaa7Va1VwqAAAAAAC7q5RyqtbaGmw3ghkAAAAAgEZ0MAMAAAAA0Mi+vQ4AAAAA0LN0ZOaszl8+nJzIWGejPZ6RxWSiu+9QeyrDTrV5m84zkSyPdlbnkqy0pzfcfyuXX3OgeZ6BC/ffO0lWenVYHU/mk5HFjaftz0KSZKo9u3aNXavPxHrGtXzt6Rxc3P6URs/r5ExOvHl6U817P2tTvRoNm+eGpZmcOnrh5Ok9r+OHO9sr7em1fSM7PIskmRzI3P/e9Jha9/wwghkAAAAAgEZ0MAMAAAAA0IgOZgAAAAAAGim11r3O0Fir1armUgEAAAAA2F2llFO11tZg+66PYC6lXFpKWSil/F53+/pSyt+XUt7Z/TPWbS+llFeWUm4spbyrlPLI3c4GAAAAAEBz+87DPb4/yXuT3LWv7WW11t8aOO4ZSR7Q/fPYJEe7SwAAAODLxNKRma13TGx/zvJoMpdkpT2dg4ub9+/PQqbas0mSYb8JfZvJcwaOH05WVqeTJCPHOm0TZ5vnZCdP72fuWclYsjq+8eD5ZKSvHr17J1m7/6DzUZ/+uvQy9h5vf23ONs/y4fX2uXRrNKzV8UwfW6/ZbT3PDUsza/ffycrq9No722+Hj2H2ZyEHrpkcKg/N7OoI5lLKvZM8M8mvnMHhz07y2tpxQ5J/VUr52t3MBwAAAABAc7s9RcYrkrw8ya0D7T/VnQbjv5dSbt9tG0my1HfMh7ttAAAAAABcgHatg7mU8qwkH6u1nhrY9SNJHpzk0Um+KskPDXnd7y6lvKOU8o6bb7753IQFAAAAAGBouzmC+aokk6WUm5K8PsmTSym/Vmv9aHcajNUk1yV5TPf45SQH+s6/d7dtg1rrq2utrVpr67LLLtvF+AAAAAAA7KTUWnf/JqU8MckP1lqfVUr52lrrR0spJcl/T/LFWusPl1KemeSlSb45nV/u98pa62O2vWiSVqtVh508HAAAAACA4ZRSTtVaW4Pt+/Ygy/FSymVJSpJ3Jnlxt/0P0ulcvjHJ55O8aA+yAQAAAABwhs5LB3OtdS7JXHf9ydscU5O85HzkAQAAAC5MS0dmkon17eXRTofCyup0Ro5tPr536P4sbNo31Z7d1DbsN6Gb5NkqyznLc3ImJ948veF+Z6uXd6o926w+fWGWRzurc92mlYwlq+OdjfnOYmRxY+7+eg3WaK/rM5jtbN+fZL1G/U60pzc3DpheXH9OPefifd7K+crzizNLQz+j7T5fg5o8L5rZzTmYAQAAAAC4iOlgBgAAAACgER3MAAAAAAA0UjpTH982tVqtai4VAAAAAIDdVUo5VWttDbYbwQwAAAAAQCM6mAEAAAAAaGTfXgcAAAAA6Fk6MrNhe/nw+vpckpWMrTesjifzndWDi53l/ixkqj277fWHnWrzgs0z0c0zur7vRHt6qGv1O7iYHGpPNcuzRZZ+c93lymo33/zW997K2dRnMM/Z1Kd9dOqs8iwf7ntfVscb5xg51llO5Cye1wWU54alTp657vbKWTyjQe2jw+ehGSOYAQAAAABoRAczAAAAAACN6GAGAAAAAKCRUmvd6wyNtVqtai4VAAAAAIDdVUo5VWttDbYbwQwAAAAAQCP79joAAAAAQM/SkZlt9y0fXl8/kbGkPZ6Rxc3HTWYhSTLVnt20b9hvQl9weU7OZHk0mUuykrGtD1od7yzn15v6c13Xntr2+uekPhPJ8uj65ly6WVfHN2Q6uEWtkuRQX76m9dlw33692iSb6jORZH8WtnxOjfPs8P6ckYnk02+ezv5t3qG9yJM3P25tc7fyHD08tvOJ3c/aTu9ykzw0YwQzAAAAAACN6GAGAAAAAKARHcwAAAAAADSigxkAAAAAgEZKrXWvMzTWarWqyboBAAAAAHZXKeVUrbU12G4EMwAAAAAAjezb6wAAAAAAPUtHZjZsLx9eX5/rLlcytvGk1fFkvrM6sphMdJv3Z2HtkKn2bJJk2G9Cn22eg4vrzecizw1LM5nr3XN1fNvjRo6t1+FMHWpPnXV91kwky6PZkPXgsZ2v1V+fpFOjXanP/Mbncqaa1uf44dMf129ldXrbfSN9NbzuIsmTJEcPj3Ua2uPbfmbORO9z1WPmg/PDCGYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaKTUWvc6Q2OtVquaSwUAAAAAYHeVUk7VWluD7UYwAwAAAADQiA5mAAAAAAAa2bfXAQAAAAB6lo7MbNm+fDiZS7KSsa1PbI9nZLGzOpHkUHtqy8OGnWrzgstzcibLo8mJwfuujp/23JFjW7dPZiFJMtWebV6fifW25dHO8kTG1nPNJyOL6/fqN9We3fb6TeqTJMdH+55Nw9pMJNnfV5tGebr1OXp4bPuDziBfz8ixTg0vpjxHD4+d/p7zZ5BlMblu4HNmat3zwwhmAAAAAAAa0cEMAAAAAEAjOpgBAAAAAGik1Fr3OkNjrVarmksFAAAAAGB3lVJO1Vpbg+1GMAMAAAAA0Mi+vQ4AAAAA0LN0ZGbrHRPJ8dFkZXU6B4+d2bX2ZyFJMtWeXWsb9pvQ2+VZPpzMZQ/ynJzJ0QPT2x8wnxxcPPM8/Vka5emvz0RnsTy63jSXZCVjyep4Mt9pG1lcO3RTnmQX6zO//b23ck7rM3DT/owjp3l/Jrt1GTTVnr0o8hx9/vbPazune8cPtaeSDP+8aMYIZgAAAAAAGtHBDAAAAABAIzqYAQAAAABoRAczAAAAAACNlFrrXmdorNVqVZN1AwAAAADsrlLKqVpra7DdCGYAAAAAABrZt9cBAAAAAHqWjsxk+fD69lzfvpWMJavjO19gvrMYWUwmkuzPQqbas2u7h/0m9IWW54almZwYuO/IsdOfN9Fd7s/Chvb+LE3yLB2Z2XCT5dHO6ly69Uk6Wfvq0G/yHOeZWXrD0LVJ1p/NoLOuz8mZDdvLo+k8v53s8E61r546Z3l6z2ov81y7NJO7HJ0e6pzTua69nsnMB+eHEcwAAAAAADSigxkAAAAAgEZ0MAMAAAAA0Eipte51hsZarVY1lwoAAAAAwO4qpZyqtbYG241gBgAAAACgER3MAAAAAAA0sm+vAwAAAAD0LB2ZWd+Y2LhveXTj9omMdVZWx5P5ZGSxszmZhUy1Z7e8/rBTbV5weU7OZHk0metur6xOb7jXMPp/nP3djI3r03ex/rqs5RyoTb/+7BPdLEma5bnA6nPtUqc+G37+AdPHFoYPl2b1udDy9H++jj5/urMyv83BZ6B9dGrDtql1zw8jmAEAAAAAaEQHMwAAAAAAjehgBgAAAACgkVJr3esMjbVarWouFQAAAACA3VVKOVVrbQ22G8EMAAAAAEAj+/Y6AAAAAEDP0smZTW3HRzvLlYwlq+Ob9o8c6ywns5Cp9uyO1x/2m9CDeZZHk7nsYZ4jm+uzId/hzW0nVqeT+b58i8lEkkPtqd3LM9GpVc9c366VjG08dnU8mV/PlST7u7U71/VJtqlRxpL2+rMcWUyuOwf1uWFp6zxz2aIOQzh4dDyH2lMXTJ7po3c4J89rq2eznRN9eUeOjp+T50UzRjADAAAAANCIDmYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaKTUWvc6Q2OtVquarBsAAAAAYHeVUk7VWluD7bs+grmUcmkpZaGU8nsD7a8spXy2b/v2pZTfKKXcWEr5i1LKfXc7GwAAAAAAze07D/f4/iTvTXLXXkMppZXkbgPHHU7yf2uto6WUq5P8TJJvOw/5AAAAgAvE0pGZZCJZHu1szyVZydjGg1bHO8v5jc0jixu3J7rL/VlIkky1ZzPsN6EvyDxnamLj5tED052V+eTg4ubD92chB66ZbJZnYvO+5dFOvXpWMrZeq575Tp0msl6Xnl2tz0De46PJyur29ellG7o+J9fz9N6hficG36VtjBwd39R2XXtq+PpcwHmaWB5NTvSeW5+Dx5q9zzSzqyOYSyn3TvLMJL/S13Zpkp9N8vKBw5+d5DXd9d9K8pRSStnNfAAAAAAANLfbU2S8Ip2O5Fv72l6a5ESt9aMDx44kWUqSWuuXknw6yd0HL1hK+e5SyjtKKe+4+eabdyU0AAAAAACnt2sdzKWUZyX5WK31VF/bvZI8L8kvNL1urfXVtdZWrbV12WWXnYOkAAAAAAA0UWqtu3PhUn46yQuSfCnJHdKZg3m1++eL3cO+LskHu/Mu/1GSI7XWt5VS9iX5xySX1R0CtlqtOuzcLgAAAAAADKeUcqrW2hps37URzLXWH6m13rvWet8kVyd5S631brXWe9Za79tt/3yttTel+IkkL+yu/9vu8bvT+w0AAAAAwFnbt9cB+vxqkv9VSrkxySfT6ZQGAAAAAOACdV46mGutc0nmtmi/c9/6F9OZnxkAAAD4MrV0ZGboc5YPdzodVjKWrI4n8532kcXkuvbUhmOHnWrzQstzw9Lp85xYnV67Z+++WxnM0iTPhvpMrK8uj66vz/Udv1VNtjg9+7OQqfbsrtRnLslKe3ptezfrc+0Z5Bm0sjp92mOmjzWrz4WW50yeV7+5bp6RY2d2/EuuOTDU9Wlm16bIAAAAAADg4qaDGQAAAACARnQwAwAAAADQSKm17nWGxlqtVh12bhcAAAAAAIZTSjlVa20NthvBDAAAAABAI/v2OgAAAABAz9KRmc7KRHJ8dOdjVzKWrI4n88nI4nr7RJL9WchUe3bTOcN+E3rpyEzngrnw8iz35Znr3X8r7fFNebZz+TUHhs+zxYWXB2o1112uZVwdz8ixzdcbrFXT+mx1/+3qM3J0fFOG7Qxbn5mlN2y77+DR8RxqTw11vUHD1udCy9N7f5YPJycGns/gczlTE8naz2Hmg/PDCGYAAAAAABrRwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjZRa615naKzValWTdQMAAAAA7K5Syqlaa2uw3QhmAAAAAAAa2bfXAQAAAAB6lo7MdFYmNrYfH01WVqc3tI0c6xy2Pws7XnOqPbu2Puw3oZeOzGzKsjyazGXrPJOnyXK2eW5Ymhnq+BMZO/1Bq+NpXz3VKM/SyZksj269b667XOnPsDq+8aD5ZGSxszqZhQ21aZLnnNenrzZN8qy9z0mOH964b2Wnew/Wqc/Isc7yuvbUbT5Po+e1Q5aepu8zzRjBDAAAAABAIzqYAQAAAABoRAczAAAAAACNlFrrXmdorNVqVXOpAAAAAADsrlLKqVpra7DdCGYAAAAAABrRwQwAAAAAQCP79joAAAAAQM8NSzOZ69teydjmg1bHT3+h+c1NBxeTy685cJvOs3RkJkmyfDg5sTrduc6xoS6xrUPtqQw7FWkvTyY271sezVrtVrpZR45tPnR/FjLVnt3y+o3yTHTufWKHezbRpD7XLs1saltpT+94zsjimV37ugsoz0TO8v3pXaTP0QPruc70HR98l0yte34YwQwAAAAAQCM6mAEAAAAAaEQHMwAAAAAAjZRa615naKzValVzqQAAAAAA7K5Syqlaa2uw3QhmAAAAAAAa2bfXAQAAAAB6lo7MJEmWDydz3baVjK0fsDqezHdWRxbXmyeS7M/Caa9/4JrJ23ye5cPr23MD+1dWpzc2zJ/5tdtHpzLsN8WXjswkE8ny6NaZ1vKcJkf76NSW7cPmuWFpZsP2lll6hqhN0qw+M0tv2PmA1fHhQiTJfOddu659AeTpe/eb5Ol9vjKx/THHR5OV9vSm9v7PW89EkkPt9XfJzAfnhxHMAAAAAAA0ooMZAAAAAIBGdDADAAAAANCIDmYAAAAAABoptda9ztBYq9WqJusGAAAAANhdpZRTtdbWYLsRzAAAAAAANLJvrwMAAAAA9CwdmVlbXz6czCVZydj6Ae3x015jZLGznBhoP9SeyrDfhL7Q8tywNLOp7UTGzijHTkYWk+vOpj7dH255dH3fXHe5oV6r3Zzz2+eYSLI/C5lqz567+qyOb3vPM9G4Pic35+mvUb8Tq9NrGXvvTM/gu5N0anTgmsldyTOXZOU85Llhaabzc/c7i+fUr310+OdFM0YwAwAAAADQiA5mAAAAAAAa0cEMAAAAAEAjpda61xkaa7Va1VwqAAAAAAC7q5RyqtbaGmw3ghkAAAAAgEZ0MAMAAAAA0Mi+vQ4AAAAA0LN0ZCbLh5O5JCsZS9rjm44ZWewsJ7Nw2utNtWc3bA871eYFl+fkzIbt46PdXEmy2s02f2bX2ir3gWsmh8vTrU/P3BbHrOVL1jMmO+Y8uJgcak+dVX22rM1p7ruVkcX1GjWpT5IcPTw23E37dbOPHNvYfF2T+lygeZIkE53F0QPTWx98mufWPjq1qc3UuueHEcwAAAAAADSigxkAAAAAgEZ0MAMAAAAA0Eipte51hsZarVY1lwoAAADw/2fv/2Nj6/P7sO/9zd6tVoUkqnWeoJvZa7gIVbjtpqWN6UqE0YwiwUUkbamME9vbGyW+ClXVrIQYDlwnGRQuRSC3j5wgdpU6TNTo3q6TW9tb5UcJRUpmUV+yUZErYRlKiwiKjYeK2xHjQE8SS+xC8SSWvv1jZsjhcPhjDjmXfB6/XsCDOed7vuecNz/nzP7x3e98LwDLVUo5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGKw3Us6yclqsn9D39OsjTb662kdJ52Z4ys5SpJ0+ztnbYv+EnqS5/XqzX3fSZ43vSQX63M63DrvcHD1ua3jq491xvmevthYLM9279KFTmZqtZ+p2iTJcP18eybvJONGjtLt7zSuz+R5nWbt2vvNuqlGn33xdLE8272cbM4/tp+Zuswarmfr5dGVhxvV5y55+uvZOr7nPOPntfsrWzf0vP7ZTNvIecZF32eaMYMZAAAAAIBGDDADAAAAANCIAWYAAAAAABoxwAwAAAAAQCOl1vrQGRprt9t10cXDAQAAAABYTCnlsNbanm03gxkAAAAAgEaePHQAAAAAgInBdu98p3Nz/5PVZD/J6XArrZfn7Rs5SpJ0+zsX+i/6S+hHmecWOV6vjjLk4PKx1vHoEis5uvf6TP7+JDnN2mhjuD76nMrSOr54nek/aWWqVvddn5PVZG+4dSnPtEl9Jlmma9Qkz+vNq4+fTrJck2fr+Ghue9P6XJXnQpZ3mGf3+7YuH7ji3jdpHSev+t2zfSsfvBtmMAMAAAAA0IgBZgAAAAAAGjHADAAAAABAI6XW+tAZGmu329VaKgAAAAAAy1VKOay1tmfblz6DuZTyiVLKUSnlp8b7P1FK+cVSyldLKT9ZSvmGcfvXlVL+Uinlg1LKz5VSfteyswEAAAAA0Ny7WCLjjyX55an9P15r/Z/WWv8nSf6/SX543L6Z5G/UWleT/JkkP/oOsgEAAAAA0NCTZV68lPKZJN+T5J9N8k8mSa31dHysJPn6JJM1Or43yfZ4+yeT/J9LKaV+lNfwAAAAABYy2O6NNjqjj5PVZD/JadYuduyvp3V8samTZCVHSZJuf2fu9RddanM2z+vV0eeFPMP15CDvJs+b3rXHT8b59oZbV3eakzVJXvW7zeszqzM/217W0tpdv9RlUqdp3f7OvddnkiO5pkb3WZ83vbN3Jpl6b4brN94vOa/RdH2m36VF87wd9LI/tf/QeS69P3PemyTZ+/I17/PM6c/73cZ5aGbZM5j/bJI/meS3pxtLKa+S/GdJfneSf3Hc3EoySJJa699K8htJfseS8wEAAAAA0NDSBphLKZ9P8mu11sPZY7XW70/yd2e0dMYfXvC6P1hK+Uop5Ssffvjh/YQFAAAAAGBhy5zB/PuSbJRS/lqSv5jkO0op//rkYK31t8bt/9C46STJ0yQppTxJspLkv5i9aK31x2ut7Vpr+7333ltifAAAAAAArlPexRLHpZRvT/Inkvwvk/w9tdYPxmsw/3NJUmv9E6WUH0ry99Za/2gp5QtJ/kCt9Q9dd912u12tpQIAAAAAsFyllMNaa3u2fan/yN+8HEm+WEr5pvH2LyaZrNL9E0n+tVLKB0n+yyRfeMfZAAAAAABYwDsZYK617idn/0jl77uiz99M8gffRR4AAADgcRps9853Osnr1eQ0a2dNrd316cNJkpUcJUm6/Z0br7/oL6HP8oxv9uB53vRu7jTH69XkG3e3zvY3xhmndfs7zerTufr4Wb3662kdj9o6Oa/R9L3neRf1uU1tkuXUJ0lOVs+397KWDNev7JskrZejSz7vdz8eecZeb55/t1q761c+h3nu6/2hmWWuwQwAAAAAwMeYAWYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2PtdrtarBsAAAAAYLlKKYe11vZsuxnMAAAAAAA08uShAwAAAABMvD/oJUlOsza/w3A9OTjfbR2PPjtJVnKUJOn2d668/qK/hH5seQbbvUttJ5vJ/mzG/vrZ5rPjq6/3vN+9vzyd5GR1lCWTPMNxjoPz2kx1P3NVre6jPsmcGvXX0zq+mGGe+6jPyebt+u7Pafv87ta1uT4OeWa93kxO+1vXvrfXZZhm5YN3wwxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGik1FofOkNj7Xa7WksFAAAAAGC5SimHtdb2bLsZzAAAAAAANGKAGQAAAACARp48dAAAAACAicF2r9mJneRkNdnrb+XZ8ahpJUeXuj19sfHxyDO+/k32x5/fuLuVzjX9nve7SZJFlyK9VJ/xTeZlm2Q5narJtJUcpdvfudDWOM8t6nOWZ7iV1ssspT5vB9e/P3tZO9vud//0Qtf+uOXZv6bf53e3Fs6ykqOFv180YwYzAAAAAACNGGAGAAAAAKARA8wAAAAAADRSaq0PnaGxdrtdF13bBQAAAACAxZRSDmut7dl2M5gBAAAAAGjkyUMHAAAAAJgYvOmdbZ+sJvtJTrM2ahiujz4PRh+t44vndpKs5OjSNbv9nbPtRX8J/VjznKwme5Mc01mucnC+eVXObn9n8TzbvdEFrnAhZ3/90v3n1ei+ntfr1TnPap5rajOdsXF9xnY3R1lau+tn123q45hnrk6y+3QrrZfzD7/qd6893coH74YZzAAAAAAANGKAGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrhbrBgAAAABYrlLKYa21PdtuBjMAAAAAAI08eegAAAAAABODN72z7deryWnWRjvD9SRJ6+Xtr7WRo3T7OxfaFv0l9GPMc7Ka7E1yTGXJwdXnPTtOnve7N15/4TzbvZxsjrYvZJo1XL+Ur3V8uVtn/LmSoyTJ0xcbi+WZV5/J/ZNra5SM6pRcXasm9UmS15vzj5/Oe45Trnu/XvW7H/k87w96F+85a06G22i9bJaHZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m63q7VUAAAAAACWq5RyWGttz7abwQwAAAAAQCMGmAEAAAAAaOTJQwcAAAAAmBhs95LO+f7u6trFDsP1tF6ONjdydOvrdvs7SZJFl9q8MU9/Pa3jd5vnZHO0vT9uOx1uJQfnfSZ5OklWZjJN7nuVRvWZ3CzJyepMtqwlw/Ur8+WGjAvnedO7kOGm+7+T+nRu7jfP7q9s5dnxxbbn/e7HKs/bQW9u+/7U9jfubl0Zefb5zXr6YmOhPDRjBjMAAAAAAI0YYAYAAAAAoBEDzAAAAAAANFJqrQ+dobF2u10XXdsFAAAAAIDFlFIOa63t2XYzmAEAAAAAaOTJQwcAAAAAmBhs927XsTP6OFk9b9off54Ot9J6edblzPN+N4v+EnpZeVZylG5/Z/E8b0Z5Xk/d5zRrlzsO1y/stl5ejLqSoyRJt79zoV+j+nQu/t1JsjfJNJ3jIGkdn2fINTka55mpz33Wpkmet4Pe/FosoPUyedXvzj32Uc8zeV7zvF5NvnF369bXmv2+r+QoT19sLJSHZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGDDADAAAAANBIqbU+dIbG2u12XXTxcAAAAAAAFlNKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBisN273NhJTlbPd/eylvTX0zpOOklWcnTl9br9nQv7i/4S+lZ5hlvJQd5ZnpPN0fZ+ktOsze/YX7/U9Oz46us+73cb50lyqSZzs83JlJzXbTrHxLusT3J1jRrX582c92fG69XLbVfmTpL+ep4djzJ91PO8HVzMs3fdfcb3usq8Z/fZF08XykMzZjADAAAAANCIAWYAAAAAABoxwAwAAAAAQCOl1vrQGRprt9t10bVdAAAAAABYTCnlsNbanm03gxkAAAAAgEYMMAMAAAAA0MiThw4AAAAAMDHY7iVJXm9ebD8dbp1tt16et3euudZKjpIk3f7OWduiS23eV55JljvnedO7sH+ymuxP58paMlxPDpLW8e2uOcm8kqM8fbGxcJ7XqzP3T0YZpl2TpzO+93RdJu5Sn+lcZ9kmuRasz+T5LVyf7V5Opt6d/YXOHr9nB+f7/d3uheNN3ufHlOf9wfnzOh1u5dnLazovqMn7TDNmMAMAAAAA0IgBZgAAAAAAGjHADAAAAABAI6XW+tAZGmu323XRtV0AAAAAAFhMKeWw1tqebV/6DOZSyidKKUellJ8a778upfyVUsp/VEp5WUr55Li9lFJ+rJTyQSnlq6WU37vsbAAAAAAANPfkHdzjjyX55STfNN5/neT7xtv/tyQ/kGQ3yXcl+Zbxf986bvvWd5APAAAAeCQGb3o5WU32rzh+mrVkuH7ecHD99VrHyUaO0u3vJEkW/SX0QnluyHIfed4Oetmf3HdWfz3Pjhe63Jnn/W6jPPPqMzfbrP75M5zOvDJVmyZ5rq1Pkme763Pbb9K4Ptu9vN483z/tb51nueJZreToVtfu9nc+FnmSZHdz7dKxuz6rZPHnRTNLncFcSvlMku9J8q9O2mqtP13Hkvx8ks+MD31vkj8/PvQ2yTeXUj69zHwAAAAAADS37CUy/mySP5nkt2cPjJfG+EeT/LvjplaSwVSXXx23zZ73g6WUr5RSvvLhhx/ee2AAAAAAAG5naQPMpZTPJ/m1WuvhFV3+pST/r1rrv7/IdWutP15rbdda2++9996dcwIAAAAA0Mwy12D+fUk2SinfneRTSb6plPKv11q/r5Tyf0jyXpL/zVT/kyRPp/Y/M24DAAAAAOARKqOlkJd8k1K+PcmfqLV+vpTyA0n+8STfWWv9r6b6fE+SH07y3Rn9434/Vmv93HXXbbfb1WLdAAAAAADLVUo5rLW2Z9uXOYP5Kv9ykv9Pkv+glJIk/2atdSfJT2c0uPxBkt9M8v0PkA0AAAAAgFt6JwPMtdb9JPvj7bn3rKOp1D/0LvIAAAAAj9Ngu3e+00lOVuf32x9/nmYtGa4nB6P9Z8fnfZ73u5fOW/SX0HfJ0zpOOlN97iXPm95Zhr2sXe4wXB99Htx8rdbxxf1X/W6jPBPTtdkff55OZ5xkm5jKOJtlI0fp9nfuVJ/92ftPZ3iH9bmyLv31OWdcvu+0jRwlSbr9nSR3e38eQ57e4EvXd5jKdJscE03z0MzS/pE/AAAAAAA+3gwwAwAAAADQiAFmAAAAAAAaKaOljz+a2u12tZYKAAAAAMBylVIOa63t2XYzmAEAAAAAaMQAMwAAAAAAjTx56AAAAAAAE4Pt3vlOJzlZPd/dn+l7mrXRxnA9Obh8rdbx+fZGjtLt72TRpTZvm+csy5LzvB30LuzvDbdGG3Put4j+bjdJmtVnpi7JeW1uU5eJreNRTabduT5Zu/G+t3Gn+sw42bzduzz9vszzqt/9yOeZfV4Te5P799cXut60/u7ieWjGDGYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2PtdrtaSwUAAAAAYLlKKYe11vZsuxnMAAAAAAA08uShAwAAAABMDLZ7SWe0/Xo1Oc1aMly//qSD883W8cVDGzk62+72d7LoL6EneU5Wk/08gjxveknO88w6zdr8E6/J3Ho5KvlKjvL0xcZiebZHeSbPbGI231mu6RwHo/pMn7qSo3T7O2f7TeuTjN6fWXPrc4vaJMnzfvdOeU7GefaGW2fXnvaq313o2snd6vMY8rw/6C38TK7Tenkxt5UP3g0zmAEAAAAAaMQAMwAAAAAAjRhgBgAAAACgEQPMAAAAAAA0UmqtD52hsXa7XS3WDQAAAACwXKWUw1pre7bdDGYAAAAAABp58tABAAAAACYG272LDZ3kZHW0uT+n/2nWLjYM15OD0WbrOOmMm1dylG5/J4v+Evqx5Xk76N187+H6lee3Xs5vf9XvJsnd6jNVm+mMc3MdXKzHtEltmuS5rj5nWa6pTzK/Rk3rM8kzz/4C17nwXvXX8+w4ed7vfuTzvH9Nntl7tXavf27TNhp+v2jGDGYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2PtdrtaSwUAAAAAYLlKKYe11vZsuxnMAAAAAAA08uShAwAAAABMDLZ7Sedy+8nq+fb++PM0a6ON4fro82D+NbeOj5Ik3f5OFv0l9KPMkySdUYb9a3tPZboiV+t49Oc973eT5E71mZfnQk0OLt7zKis5Sre/0zxPsnh9JrVJ5uacZFo0z9tB71Lb3nBrdP2XC13qgskzewx5JjVqkmfyvF5vXj524d29ytQ73d/tXjps5YN3wwxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGjEADMAAAAAAI2UWutDZ2is3W5Xi3UDAAAAACxXKeWw1tqebTeDGQAAAACARp48dAAAAACAicF27+ZOneRk9Xx3f+rQadaS4fpo5yBpHSed8bGVHOXpi42Pfp7Ozf0mdp9upfVy/rGNHF1qW2aek9Vkb1yPeZk6GdXkTnne3OJ5Tdl9upUkc/PcR316gy/NP9Bfv9TUOl7o0nnV72bRX/Y/tjyLPq9pe18ePbt5z2li0edFM2YwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGik1FofOkNj7Xa7Lrp4OAAAAAAAiymlHNZa27PtZjADAAAAANDIk4cOAAAAADAx2O7lZDPZH++fZu384HB99Hmw2DVbx8lGjtLt72TRX0I/ujxvenm9erHtdLiV1svbnd+55tjzfrdRfaYvfDLOtp/ktL+VZ8cLXS4r47pM3LU+t6nNdTWZ1qg+b3pXHjtZTQ53t268xsZMTabdZ57Xq8k3vuM8bwe9W9XgtqafZZPnRTNmMAMAAAAA0IgBZgAAAAAAGjHADAAAAABAI6XW+tAZGmu329VaKgAAAAAAy1VKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBi8KY3t/316ujzNGvJcH1un9bL+dd81e+ebS/6S+jHmudkfP+9K+5/1b3n2cjR2fbTFxuL5dmeX590Rh+TnNPOMh+M9lvHo+4r4xzd/s5Z37vWJ0n2hltz+962RneuT+dy+2xdrsqYJM+uyPm83128Po8wz8nm+J24jZl3fd4zvMv3i2bMYAYAAAAAoBEDzAAAAAAANGKAGQAAAACARgwwAwAAAADQSKm1PnSGxtrtdrVYNwAAAADAcpVSDmut7dl2M5gBAAAAAGjkyUMHAAAAAJgYbPdyspnsj/dPs3a7E4frZ5utl+fNnSQrOUq3v5MkWfSX0I8tz9tBL3vTGYbrF64/rbPQlUc+++LpQv2n63NjbYbryUHSOr7YPJvzLvXpDb50qa21uz6n5+L1ed7vLp7ng8GNfZ7NPL/n/e6tr/+RzzP9vPqj5zT7ftzW7PNs8rxoxgxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGjEADMAAAAAAI2UWutDZ2is3W5Xi3UDAAAAACxXKeWw1tqebV/6DOZSyidKKUellJ8a7/9wKeWDUkotpfydU/1KKeXHxse+Wkr5vcvOBgAAAABAc0/ewT3+WJJfTvJN4/3/d5KfSrI/0++7knzL+L9vTbI7/gQAAAD+NjHY7iVJTjZH+/szx0+zNtoYrp83Hlzs0zq+uN9JspKjdPs7WfSX0I8uz5vehf3Xq+cZWrujDBs5WuiaE43r07n6+Mlqspe1ZLie1svbZev2d86271qfJNldXUsyqk/T2kxyNX1/JjU6WR197k3emytMnmVyfc2evthYPM/U83rwPOPnNXlGW7ufWuj8q0zeISsfvBtLncFcSvlMku9J8q9O2mqtR7XWvzan+/cm+fN15G2Sby6lfHqZ+QAAAAAAaG7ZS2T82SR/Mslv36JvK8lgav9Xx20XlFJ+sJTylVLKVz788MN7CQkAAAAAwOKWNsBcSvl8kl+rtR7e53VrrT9ea23XWtvvvffefV4aAAAAAIAFlFrrci5cyv8xyT+a5G8l+VRGazD/m7XW7xsf/2tJ2rXW/3y8/68k2a+1/oXx/l9J8u211r9+1T3a7Xa1lgoAAAAAwHKVUg5rre3Z9qXNYK61/jO11s/UWn9Xki8k+cuTweUr7CX5x8rItyX5jesGlwEAAAAAeFhP3vUNSyn/REbrMv/3kny1lPLTtdYfSPLTSb47yQdJfjPJ97/rbAAAAMDDGmz3LjZ0kpPV0eb+VPNp1i72G66n9fLslCTJSo6SJN3+zlm3RX8J/djyvB305rbvz2YYricHV1/n2fHF/ef9bqM8F+ozVZtJpkznGq6PPmdytY7PazSdpUmex1af9we9y/dd0OQ9mvbqY5JnsN3Lyeb8Y3v9rbPt2edxk6bPi2beyQBzrXU/4/9dqbX+WJIfm9OnJvmhd5EHAAAAAIC7W9oSGQAAAAAAfLwZYAYAAAAAoBEDzAAAAAAANFJGSx9/NLXb7WqxbgAAAACA5SqlHNZa27PtZjADAAAAANDIk4cOAAAAADAx2O7lZDPZH++f9rduPKd1nLzqd291/UV/Cf0Y85zpnG+erN587l7Wkv76hbb+7sWcjfN0LmbYT3KatWQ4vt/BqC7jrkmS57eo0Z3qM3Wz29RnP8npcCs5GO3P1uYueU42r+6zl7X5B4bnz6r18vLhV/3uRz/Pm3GeWzyfafsZv1/TZt7tJHmx+XSxC9OIGcwAAAAAADRigBkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGim11ofO0Fi73a6LLh4OAAAAAMBiSimHtdb2bLsZzAAAAAAANPLkoQMAAAAATAze9PJ6dbR9mrWkv37jOa3jZCNHZ/vd/s6VfRf9JfRjzDPrZJxvP8npcCs5uD7brEnWbn9n8TzbU3k655uvV8f1Gq4nB/PvO+m+MnX/WfdVn/3x9nX1mZcxucf6zObaHH3uZ6pWyYV8V2Wa+KEXT+81z/54+13meb05/9hN7/K8PK/63Qv7Vj54N8xgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m63q7VUAAAAAACWq5RyWGttz7abwQwAAAAAQCNPHjoAAAAAwMRgu3d9h05ysjra3B83nWYtGa4nB+fdWsejz40cpdvfOWtf9JfQH4k8Uxkm9i/3GmXrb1157dZx8kMvni6ep3P18ZPVOXWZODi/78RGjpLkrEZ3rk/nPMe0/Vx2mrWkvz7nyCjjq3538Txvetn78nnNJ3/fTabfket85PNc9/264r3aXV073xk/r/5u917y0IwZzAAAAAAANGKAGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrhbrBgAAAABYrlLKYa21PdtuBjMAAAAAAI08eegAAAAAABOD7d78A53Rx8nq6HN/3Hyatasv1l9PkrSOk40cpdvfyaK/hH6UeTrn972N/Umu4XpycPFY63j0+arfTZLGeWZN59u/4tyzTMlZrtbx6HIrTevzpnfp/jfZz8PWZ9redE2mc7y83PdVv7v8PMOtue33left4Irv1w32k5xekW2i/4XF89CMGcwAAAAAADRigBkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGim11ofO0Fi73a4W6wYAAAAAWK5SymGttT3bbgYzAAAAAACNPHnoAAAAAAATg+3e1Qc755snq6PPveHWeePB6OPZcbKSo0und/s7WfSX0I8uz5teXq8mp1lLa3f9qjiXzLv/dI6Ju9bnZHP0uX9F/9OsjTaG68lB0jqen3slR3eqz9m9+utpHZ8fv2uN7vP9mdRq1l5/K1vHV2eZzvRRz/P+4Oo8n9/duvaZLCMPzZjBDAAAAABAIwaYAQAAAABoxAAzAAAAAACNlFrrQ2dorN1uV2upAAAAAAAsVynlsNbanm03gxkAAAAAgEaePHQAAAAAgInBdi/pJCeryX6S06ydHxyuJwfnu8+Ok5UcpdvfufX1F/0l9KPMM9GZ3+dk9XbX2sta0l/P1vF55oXzvOnNved+pmo1XD8/cJBLpu8/q3F97libJNnrb6V1nGzkDvUZvz/3YfdXtpIkrePRJZ/3ux/5PG8HvZs7je1n/E7NfO/m2To+SpI8fbGxUB6aMYMZAAAAAIBGDDADAAAAANCIAWYAAAAAABoxwAwAAAAAQCOl1vrQGRprt9t10cXDAQAAAABYTCnlsNbanm03gxkAAAAAgEaePHQAAAAAgInBdu9s+2Qz2RtuJUlaL5ONHN3qGt3+zpXHFv0l9KPL82aU5/VqcjrOcsnB7a/37Hj0uZKjdPs7zerTSU5WR/v7U8dOszbaGK7fmK81zjGp6aRm916fBWqTjOqzMpWpaZ7kco1O+1tn91jEytR79/TFxkc6z9tBb/SdWvC5XGfr+Kjx+0MzZjADAAAAANCIAWYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2PtdrtarBsAAAAAYLlKKYe11vZsuxnMAAAAAAA08uShAwAAAABMDLZ713foJCerF5v2x5+nWUuG68nB+bHWcdJJ8rzfTZIs+kvoR5mnM9o+WU2+YXdrofOvci/1mcq1l7XRTn89reOL53SuuNZKjpIk3f7OWdtd6vMbX76f2iR3qM+bUX1eryanw8XytF5efezVxyXP1PNKxu9OfyvPjq8+5zZWcpRuf2fhPDRjBjMAAAAAAI0YYAYAAAAAoBEDzAAAAAAANFJqrQ+dobF2u12tpQIAAAAAsFyllMNaa3u2fekzmEspnyilHJVSfmq8/98vpfxcKeWDUspfKqX8t8btXzfe/2B8/HctOxsAAAAAAM09eQf3+GNJfjnJN433fzTJn6m1/sVSyr+cZDPJ7vjzb9RaV0spXxj3+8PvIB8AAADwSAy2excbOotf42R19Hm4u3V2ief9bpJk0V9CP8Y8rzeT0+HWpWOtl3eKmef97p3rc7J5vr0//jzNWjJcPz9wkLSOL15nOu9KjpIk3f7O4nne9PJ69eb6zN7zNu5cn87oXdif6XOatcsnTtcrl7Mnyas75pk8q8eSZ2L3+y4+u9u+1ys5Sre/c6HNygfvxlJnMJdSPpPke5L8q+P9kuQ7kvzkuMsXk/yD4+3vHe9nfPw7x/0BAAAAAHiElr1Exp9N8ieT/PZ4/3ck+fVa698a7/9qktZ4u5VkkCTj478x7g8AAAAAwCO0tAHmUsrnk/xarfXwnq/7g6WUr5RSvvLhhx/e56UBAAAAAFjAMmcw/74kG6WUv5bkL2a0NMb/Kck3l1Imaz9/JsnJePskydMkGR9fSfJfzF601vrjtdZ2rbX93nvvLTE+AAAAAADXKbXW5d+klG9P8idqrZ8vpfzfk/wbU//I31drrf9SKeWHkvy9tdY/Ov5H/v5ArfUPXXfddrtdLdYNAAAAALBcpZTDWmt7tn3ZazDP808l+SdLKR9ktMbyT4zbfyLJ7xi3/5NJ/ukHyAYAAAAAwC09ubnL3dVa95Psj7d/Jcnn5vT5m0n+4LvIAwAAADxOg+3exYZOsru6Ntrur6d1nHTmnPe8373V9Rf9JfSjzdNJTlZv7r+XtfkHhutJktbL0e5GjtLt7zTOc7I52t9Pcjp7z/74XseXz5/c9yp3eV6TTPNcWZfkyto0yfN2cPH92U9yOty61K/1cnSf27qvPHtZO/t7pz17may8ozzXPot55uQ9c5BsHTd/XjTzEDOYAQAAAAD4GDDADAAAAABAIwaYAQAAAABoxAAzAAAAAACNlFrrQ2dorN1uV4t1AwAAAAAsVynlsNbanm03gxkAAAAAgEaePHQAAAAAgInBdi/pjLZPVi8e25/T/3S4lRwkrePRaSs5Sre/c+X1F/0l9KPMkySd8zz7SU77W9ee1zpONnJ05fFJxqb1ma3NrP3x52nWRhvD9fODB8mz41GtprM0zpNcyrQ/uXd/fc5Z19fnLnneDnqX//amhutn79arfvfR5Hl2nDxvmOfseU3Z/b7z79A8857TVd8xKx+8G2YwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjpdb60Bkaa7fb1VoqAAAAAADLVUo5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGKw3bvc2ElOVpO9rJ23DdeTg6R1nHSSrOTo7FC3v3Pl9Rf9JfRjy/N2MCdPkv0kp8Ot+SeNc23k6NosTfKc1We6JsP1JEnr5dmhJBdrMnHfeWbrs5/5dZlk2xhnuilH0zxz35+xk82Zd2iO1u76Wf1mPe93P9Z5LmXrj57j1vHtvlvJ4s+LZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGDDADAAAAANBIqbU+dIbG2u12tVg3AAAAAMBylVIOa63t2XYzmAEAAAAAaOTJQwcAAAAAmHg76F1q25/aPs3axYP99fPN3e6N11/0l9CzefZnjr/rPIM3l+tzW69Xk9PhVlovk40cJUm6/Z275dke5+nccN/+VlrH591WxvefNpulUZ57rM+95Nnu5WTz6uN7w608e7lo0lH9uv2de82zn9Hf/67zJLnw/rxend/3NGtJfz2t44vtG1Pv0l3fZ5oxgxkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGim11ofO0Fi73a7WUgEAAAAAWK5SymGttT3bbgYzAAAAAACNGGAGAAAAAKCRJw8dAAAAAGBi8KaX16vJadYuHxyun222Xl5/nY0cXWrr9ney6FKb95Gnk2TlnvK8HfSuPLY3L+PEOOuzqZzzMj19sbFQnsF2b/QHjp2sXt33Ur7henKQtI4v1qjb3znr8s7qM8mT8xrdS33ejPKcrCb747a579IttHbX86rfvdDW5H2+rzxbu5+68Kya5Jl9Xjc+o4mp7948rZfJq3534Tw0YwYzAAAAAACNGGAGAAAAAKARA8wAAAAAADRSaq0PnaGxdrtdraUCAAAAALBcpZTDWmt7tt0MZgAAAAAAGnny0AEAAAAAJgbbvbPtk81kP8lp1pLhenJwsW/rOOlM7a/k6Nprd/s7WfSX0Ffm6a9f6vsu8rw/6M1tP83a+c7wcrbrtF4mr/rdJGlen/Ef/nr1ikyzucbPsnV83tRJ8nycY+LB63MwyniX+pxsXm7fn800zxU57/q87jtP/wvnz+wu368keT3OdjrcWug6ycW6NM1DM2YwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGik1FofOkNj7Xa7WqwbAAAAAGC5SimHtdb2bLsZzAAAAAAANPLkoQMAAAAATAy2e0knOVkd7e8nOc3aaGe4nhxcf37rONnIUbr9nbnHF/0l9KPMM+Vkc/S5l7VRnnnmZGwdjz5ns90pT2ecafVyv/3crm6z9bqv+iTJ3nBr/kk31CdJ4zxvB72bO2X8/GYz7F7xPHNeo8eS51W/m6TB83pznmfee3OVvf4Vz3JKf7e7cB6aMYMZAAAAAIBGDDADAAAAANCIAWYAAAAAABoptdaHztBYu92u1lIBAAAAAFiuUsphrbU9224GMwAAAAAAjRhgBgAAAACgkScPHQAAAABgYrDdu9jQGX2crCb7c/qfZi0ZricHSev4rPslKzlKt7+TRZfafHR53vRysjra3k9yOtxa6Pz+F7rXHr+P+kzyTeyPP89qM3GQPDu+2Pd5/2K+JnlONi/ee5Eabb0cPZerNHleu79yu/u3jq8//qp/+dl91PO8P+jNbV/0vT5zcJ57I0d5+mKj2XVYiBnMAAAAAAA0YoAZAAAAAIBGDDADAAAAANBIqbU+dIbG2u12XXRtFwAAAAAAFlNKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBisN1Lkpxsjvb3x+2nWbv6pOH66PPgYnPrOOmMt5/3u0mSRX8J/RjzTLLsDbeu7dt6efWxV+P7z2pan7k6ycnqaHM/45pN1aZ1fNbtzEqO0u3v3DnPXWt0X/V5fzC/Phfen0lNZk3VaCNHc7s8fbHxbvKM3+X7ztP7YLBQ/4nr3u1pP/TiaaPrsxgzmAEAAAAAaMQAMwAAAAAAjRhgBgAAAACgEQPMAAAAAAA0UmqtD52hsXa7XRddXB0AAAAAgMWUUg5rre3Z9qXNYC6lfKqU8vOllF8spfxSKeVHxu3fUUr5D0sp/1Ep5YullCfj9lJK+bFSygellK+WUn7vsrIBAAAAAHB3T5Z47WGS76i1fq2U8skkP1tK+feSfDHJd9Za/2opZSfJH0nyE0m+K8m3jP/71iS7408AAADgbxOD7V5ONpP9JKdZS4brSZLWy4v9OklWcpQk6fZ3bn39RX8J/RjznN1wQSero8+9rCX99bSOzy+1kqN0+zvN88zJNLlfMlW/ZFTDg/NjrePzU2dr+GD1Sc5qdKf6vDmvz+vVazpOOR1uXXms9TLZmKrRsvNMv/PLznMXs89ua/dTjfLQzNJmMNeRr413Pzn+77eS/Ne11r86bv9ykn9ovP29Sf78+Ly3Sb65lPLpZeUDAAAAAOBulvqP/JVSPlFK+YUkv5bRYPLPJ3lSSpms1fEPJ3k63m4lGUyd/qvjttlr/mAp5SullK98+OGHS8sOAAAAAMD1ljrAXGv9rVrrWpLPJPlckv9xki8k+TOllJ9P8v/LaFbzItf88Vpru9bafu+99+47MgAAAAAAt1Rqre/mRqX8qSS/WWv956fa/hdJfqDW+odKKf9Kkv1a618YH/srSb691vrXr7pmu92u1lIBAAAAAFiuUsphrbU92760GcyllPdKKd883v76JL8/yX9cSvm7xm1fl+SfSvIvj0/ZS/KPlZFvS/Ib1w0uAwAAAADwsJ4s8dqfTvLFUsonMhrI/lKt9adKKf9cKeXz47bdWutfHvf/6STfneSDJL+Z5PuXmA0AAAAAgDt6Z0tkLIMlMgAAAODjZfCml5PVZD/JadbODwzXL3Y8ON9sHZ9vd5Ks5OhC125/52x70XGER5dnu7dQ/5PN8+294dZZztbxKFtm8j19sXH3PJ3zzbPaDbfSennh0KV7TzxEffaTnPa3zp7dvNoki9fn/UEvp8Ot23U+uLnLxNbxUbr9nYXr89jyXHhenWT3Vy5nm7yr896VadPvzYRxw/v1zpfIAAAAAADg480AMwAAAAAAjRhgBgAAAACgEWswAwAAAABwLWswAwAAAABwr548dAAAAACAicF272z7ZDPZT3I63EoOktZx0kmykqN0+zuNrr/oL6Efc55pJ5u3O39vnP3Z8fzjn33xdOE8k3vvj9tOszbaGK5f6t96efkanant5/3uhWP3UZ9b1yZrSX/97LnOet7vLpzn7eA8z/41/c5qdlv99fR3H0+erd1PpdvfuVOeJvbHn6fjZzd5r1dylCR5+mLjTtfndsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGDDADAAAAANBIqbU+dIbG2u12XXTxcAAAAAAAFlNKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBi8KaXk9Vk/xZ9T4dbycFou3WcdJI873evPWfRX0I/ujzbvfkHOsnJ6vnu/iTPlGcvL5+2kqMkSbe/0yzPm1Ge6RqdZu28w3D9fPtgVJeZ2BdyTGdplGdefWZqs5e1i7lyu9o0yfN2MP957eeaOiVpzcnzas679FHPM/u8TjZvd95+Lr/fWy+PLjyrJnloxgxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGik1FofOkNj7Xa7WksFAAAAAGC5SimHtdb2bLsZzAAAAAAANGKAGQAAAACARp48dAAAAACAicF273ynM/o4WR197s/0Pc3a+c5wffR5cN7UOh59buTorO3pi40HzdNJsnLHPCebF+99Oty60Kf1cvQ5/XffRre/k0WXIh1s987qMvF6dVyL4XpycP4crsrU7e9cef0mea6rz6Q2V2W5yiRjo/okl2o0z+S9usre+O94Nv4bnve7H/k87w96N3e6wrz3fiNHF94nS+u+G2YwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjpdb60Bkaa7fb1VoqAAAAAADLVUo5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGLwppckOVlN9qfaT7N2seNw/Xz7IGkdJxs5mnvNbn/nbHvRX0I/ujzbvUttJ5uX++1lbZTp4LytdZx0kjzvd6+8/p3zdMaZVkef+5mq1aRG4/rMOe1Sto96fd4OeqN7Laq/fnOX3e5HPs+85zVXJ9ldXTvbbe2O8ry65lkliz8vmjGDGQAAAACARgwwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjpdb60Bkaa7fb1WLdAAAAAADLVUo5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGLwpnep7fXqxf3T4VZaL8/3N3J04Xi3v3Pl9Rf9JfSt8vS30jp+R3m2Z/J0Rh8nq5f7XmVvKm8nyfN+917zTGfZy1pau+vTUa81neVe8szJdJPD3a3JaZcyNcnzevP6PqdZW+iaGa6n/4XuxyLP28Gc53VLezPfu2mv+s3y0IwZzAAAAAAANGKAGQAAAACARgwwAwAAAADQSKm1PnSGxtrtdrWWCgAAAADAcpVSDmut7dl2M5gBAAAAAGjEADMAAAAAAI08eegAAAAAABOD7V7SOd8/WZ3fby9ryXA9SdJ6ed4+OXUlR+n2dy6dt+hSm48yzz052Rx9fsPuVpJRxqcvNu4nz0zN9pOcTmp0kLSOL3TJSo7Otqfr9ND12etv5dnxaP9e63OV//rbbuzyzurTSfLlJed5c3Weq75r8+yPP0/7W9k6PjrLZWndd8MMZgAAAAAAGjHADAAAAABAIwaYAQAAAABopNRaHzpDY+12u1pLBQAAAABguUoph7XW9my7GcwAAAAAADTy5KEDAAAAAEwMtntn2yeb5+17w63kINk6Pkq3v9P4+ov+EvqmPP3dbuMsjfK86eVkdbS9P247zdqtzm3trqdzxbHn/e6d80xnupRruD76PEhax6PNjdz8LBfN83Zw/rwmWW5bnwzX8+zl5eZJbe6aZ9r+zP6ljMP15OBiU+s4edW/+L49VJ7JM7xrnsn3a3dz7fqON3i2u362fZfnRTNmMAMAAAAA0IgBZgAAAAAAGjHADAAAAABAIwaYAQAAAABopNRaHzpDY+12u1qsGwAAAABguUoph7XW9my7GcwAAAAAADTy5KEDAAAAAEwMtntJ5+rjJ6vJXtZGO8P10efB5X6t4/PLPO93z9oX/SX0Y8yzu7l25fHW7vqtrzX7Zz3vd5vVZ+xkc/S5P94/vaYureOr86zkKN3+TpLF6/N20Dt/HrP663Pve5X7qM/bQe/mThnV7PSq3NOmavnsOPnsi6cf6Tx/rje4sc81X78bLZqHZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m6366Jr3wAAAAAAsJhSymGttT3bbgYzAAAAAACNLG2AuZTyqVLKz5dSfrGU8kullB8Zt39nKeU/LKX8QinlZ0spq+P2ryul/KVSygellJ8rpfyuZWUDAAAAAODunizx2sMk31Fr/Vop5ZNJfraU8jNJdpN8b631l0sp/9sk//skz5NsJvkbtdbVUsoXkvxokj+8xHwAAADAIzPY7iWd5GT15r77SU6zdt4wXE/r5fluJ8lKjtLt75y1LbrU5qPMc4WTzYUudZa3tbueV/3u3fJ0ru4zXbv98efpcCutl+c1mTWp0celPlfde/+K806HW/MPHCT93e7ZbpM819XhseW5Ktfke7a1+6kL7dPfrSZ5aGZpM5jryNfGu58c/1fH/33TuH0lyX863v7eJF8cb/9kku8spZRl5QMAAAAA4G6WOYM5pZRPJDlMsprkz9Vaf66U8gNJfrqU8l8lOU3ybePurSSDJKm1/q1Sym8k+R1J/vOZa/5gkh9Mkt/5O3/nMuMDAAAAAHCNpf4jf7XW36q1riX5TJLPlVI+m+SPJ/nuWutnkrxK8i8seM0fr7W2a63t9957794zAwAAAABwO6XW+m5uVMqfSvJfJfmjtda/Z9z2O5P8u7XW/1Ep5d9Lsl1r/Q9KKU+S/GdJ3qvXBGy329VaKgAAAAAAy1VKOay1tmfblzaDuZTyXinlm8fbX5/k9yf55SQrpZT/wbjbpC1J9pL8kfH2P5zkL183uAwAAAAAwMNa5hrMn07yxfE6zH9Hki/VWn+qlPK/TvJvlFJ+O8nfSPKPj/v/RJJ/rZTyQZL/MskXlpgNAAAAeIQG270b+5xsXtzfn9o+HW6NNg6S1nHSSbKSo3T7O0mSRX8JfZc8p1lLhusX8mzkKEnulOdkc+Zvztpooz+6V+t4/rmztZjn3uvTSU5WR5v7GWcd50wuZp3kS+5en8n9kqnncHD5nrNmn8+sRfO8HYzqszd5LxdxML/52XHyvN/9WOTpDb50fYepd+U6V71HT19sLJSHZpY2wFxr/WqS3zOn/d9K8m/Naf+bSf7gsvIAAAAAAHC/lvqP/AEAAAAA8PFlgBkAAAAAgEYMMAMAAAAA0EiptT50hsba7XZddPFwAAAAAAAWU0o5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGKw3Us6o+2T1dHnfpLTrI12huvJQdI6vvoaGzm6sN/t75xtL/pL6Nvkab28+vxOkpX7znNbnctNu6trZ9ut3fULXZ73u/eTZ859p71enapff/3Cs5yuV7e/887rk1xdo3dVn9kcz3bXz+4/62OR55r7Tz+L23i2u34hl5UP3g0zmAEAAAAAaMQAMwAAAAAAjRhgBgAAAACgkVJrfegMjbXb7WotFQAAAACA5SqlHNZa27PtZjADAAAAANCIAWYAAAAAABp58tABAAAAACYG273RRue87WQ12Z/qc5q1853henIw2mwdnzdv5OjCdbv9nSTJoktt3keeTpKVe8rzdtC71Laf5HS4lf4Xugtda57G9Zk1rtd0rU6HW0mSZy/nnzJdo/uuT5L8ZLe/0LXmaVSfzqgOV9m/xXVOs5YM17P18uisNh+HPLPPa969J/da1LOXyWdfPF34PBZnBjMAAAAAAI0YYAYAAAAAoBEDzAAAAAAANFJqrQ+dobF2u10XXdsFAAAAAIDFlFIOa63t2XYzmAEAAAAAaOTJQwcAAAAAmBhs93KymexPtZ1mLRmuJ0laL0dtnQWu+bzfPdte9JfQjy7Pm96Vx05WL+ZMxln761ee8+w4WclRuv2dZnm2x3k6l+9/mrXRxnA9Obj6Gs+OL9Zk2n3V5/Xq5baz57hAtkXz9H5icGF/6/joVudNnsdNPup5rnufp+2url1suOGdnvjsi6cL5aEZM5gBAAAAAGjEADMAAAAAAI0YYAYAAAAAoBEDzAAAAAAANFJqrQ+dobF2u10XXTwcAAAAAIDFlFIOa63t2XYzmAEAAAAAaOTJQwcAAAAAmBhs90Ybnav77P7KVlrHyUaObn3dbn8nSbLoL6EH271rsyTJ3pe3kry7PCebyX6S0+HWWXvr5Y0xL1jJ0VmGafdRn9ero8/T4dalXCtX1GhelqZ5Xm+e33/i2cvbX+Oq2jTJ83bQu9S2n+Q0awtdZ9az3fU873fvJc/eHbMkydbup9Lt7yyc5/2ZPKfDrWuf1VXvzzxN8tCMGcwAAAAAADRigBkAAAAAgEYMMAMAAAAA0EiptT50hsba7Xa1lgoAAAAAwHKVUg5rre3ZdjOYAQAAAABoxAAzAAAAAACNPHnoAAAAAAATg+3e2fbJZrI/dex0uJUcXOzfOh59dsb7KzlKt79z5fUXXWrzMedJJzlZHW3ujbNM7p8kGzlKkmvvf995kvNM+0lOszbaGa5fqtXEs+Or63Tf9UnOa/RO69M5b5tkmti/5bVOh1sX9vtf6N45z2yWd53n7aB3qe2m+59mLemvX2rfOr78Dlla990wgxkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGim11ofO0Fi73a7WUgEAAAAAWK5SymGttT3bbgYzAAAAAACNPHnoAAAAAAATg+3ezZ06yclqspe1ZLieHFzfvXU8+nzV72bRX0LfJs/J5ujzIfNMMly4z+63LXTtbn/n7nk6yevV5DRro/1xPSZ/87hLkmQlR8vPMzZbn2/Y3brx/veS580V78+Xb3423f7OjX0+8nkWeJ/3Ju9UktbuejbGz++6XFY+eDfMYAYAAAAAoBEDzAAAAAAANGKAGQAAAACARgwwAwAAAADQSKm1PnSGxtrtdrVYNwAAAADAcpVSDmut7dl2M5gBAAAAAGjkyUMHAAAAAJgYbPeSJCebo/39cftp1m4+ebieHFxsenY8+lzJUbr9nSz6S+jHmGeS5Tb2Z/ZPs3aWq3WcdMZZktypPklGF0tysnrFfZPRvZO0Xl467awm05ZZn9mMSXI63Dp7ZtP1meS6a312V9cWOj9J0l+/1PTsOHne73688lzhZHP+s/pHdr/t0vsyy8oH74YZzAAAAAAANGKAGQAAAACARgwwAwAAAADQSKm1PnSGxtrtdrWWCgAAAADAcpVSDmut7dl2M5gBAAAAAGjEADMAAAAAAI08eegAAAAAABOD7d75Tud882Q12U9ymrVkuD5qPEhaxxe7r+Qo3f7OlddfdKnNq/K8Xh19vus8bwe97C/Q/zRro41JxrFnL0efz/vdO+WZ1Odkc7Q/ne10uHWx88H43uMa3VSbJnka1eeK2szLt2ieP9cbLNR/YiNHc9s/bnneH/Tmtp+9t9OG62fP5jae97sL56EZM5gBAAAAAGjEADMAAAAAAI0YYAYAAAAAoJFSa33oDI212+1qLRUAAAAAgOUqpRzWWtuz7UubwVxK+VQp5edLKb9YSvmlUsqPjNv//VLKL4z/+09LKf/2uL2UUn6slPJBKeWrpZTfu6xsAAAAAADc3ZMlXnuY5DtqrV8rpXwyyc+WUn6m1vo/n3QopfwbSf4f493vSvIt4/++Ncnu+BMAAAD428Rgu5d0kpPVy8f2spb01688t3V8vt1J8rzfvdRn0V9CP8Y8J5s399uf03aatWQ4ytt6efHYq3G2pvVJ5tdokuU0axcbh+vJweW+reOzy+V5v7twnreD3q377k9tX8g3lW3yDCfP7y7Pa/+KPtPPJQfJs+MrOs7x2RdPl5qn9fL8edxkJUd5+mJjoTxXPa+92fflllq762fvcrL4+0wzS5vBXEe+Nt795Pi/s/U4SinflOQ7kvzb46bvTfLnx+e9TfLNpZRPLysfAAAAAAB3s9R/5K+U8olSyi8k+bUkX661/tzU4X8wyf+z1no63m8lGUwd/9Vx2+w1f7CU8pVSylc+/PDD5QQHAAAAAOBGSx1grrX+Vq11LclnknyulPLZqcP/qyR/ocE1f7zW2q61tt977717SgoAAAAAwKKWOsA8UWv99SRvkvwDSVJK+TuTfC7JvzPV7STJ9MIxnxm3AQAAAADwCJVa6829mly4lPeS/De11l8vpXx9kn6SH621/lQp5Y8mWa+1/pGp/t+T5IeTfHdG/7jfj9VaP3fdPdrtdrVYNwAAAADAcpVSDmut7dn2J0u856eTfLGU8omMZkp/qdb6U+NjX0jy/kz/n85ocPmDJL+Z5PuXmA0AAAAAgDta2gBzrfWrSX7PFce+fU5bTfJDy8oDAAAAPH5vB70kyf54/zRryXD9+pMO5je3js+3X/W7SZJFfwn92PIMtnvzD3TON1+vJqf9rWvvP3NKkuR5v9ssz8yFTlZH9ZquVevl/MgrObrQ1u3vXNhfRn12n25dekaztZk5JSs5Sre/0yjPyeZCp5zZz+g5PpvKNskx8a7zfH734nt11zxvB73szXlXp9303l7nsy+e3tyJO3snazADAAAAAPDxY4AZAAAAAIBGDDADAAAAANBIGS19/NHUbrfromu7AAAAAACwmFLKYa21PdtuBjMAAAAAAI0YYAYAAAAAoJEnDx0AAAAAYGKw3Us6ycnqaH9/6thp1kYbw/XkIGkdj3Y711xvJUfp9nfO9hddavPR5XnTu7HP7up5rtbL2123k+R5v9usPpMLjE3X6jRrZ/VJzms0c8qZlRwlyVmNllmf1u76ra7ZyflzWzRPb/ClpH+7+0ybrdPzfnduv4XzfDA4exaPIc+853WyOn53hlsLXWui9TJ5Nc5nad13wwxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGik1FofOkNj7Xa7WksFAAAAAGC5SimHtdb2bLsZzAAAAAAANPLkoQMAAAAATAy2e6ONznnbyWqyn+Q0a8lw/dI5rZfn25PTVnKUbn/nUt9Ffwl9H3lWcpQk95bn9WZyOty6ss/0/WdN55k2yXbX+pysJnszdbnq+czee577rs91tUmSjTm1SZrX5/1B79rj37h79XNMklf97rXHPw55Zp/VTc/oOhsz33srH7wbZjADAAAAANCIAWYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2PtdrtarBsAAAAAYLlKKYe11vZsuxnMAAAAAAA08uShAwAAAABMDLZ7lxs7o4+T1dHn/tSh06wlw/XRzsF5e+v47LSs5Cjd/k6SZNFfQj/aPJ3z+0/bv9x0yed3ty7sP+93z7ab5jnZnH98kuc0a0l/VJerajHPsvJcZ7o+s/ma5Jlk2Zu8GwejGkx05p55s+f97kc+z9vBnO/XlP0GOU6HW3n2slkemjGDGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrtZSAQAAAABYrlLKYa21PdtuBjMAAAAAAI0YYAYAAAAAoJEnDx0AAAAAYGKw3RttdOYff72anGYtGa4nSVovR+0bOUq3v3Pj9RddavOx5jnZHO3v9bcu9Wkdn293kjzvd299/SZ5TjaT/YzrMG24nhyc55mUcCVHSbL0+uwNt5KD82PTdbnt87mvPNfZfXr5GV7l2fHF53nveTrJ7q+8uzxvB7257/B1pp/jxPTXc2Xq2Vpa990wgxkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGim11ofO0Fi73a7WUgEAAAAAWK5SymGttT3bbgYzAAAAAACNPHnoAAAAAAATbwe9JMn+eP80a8lw/eoTDpLW8fXXfNXvnm0v+kvox5Zn8KZ3qe1kNWcZT/tbeTa+//Op+9zWwnm2Z/J0zvOcZZrU7GD+NVrHSWembSVH6fZ37lyfS1mm6jOxSJ2a1udkc/7xvawl/dH7tHV8tNC1G9XnseV508vr1YttN70vi3ix+fTuF+FGZjADAAAAANCIAWYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKTUWh86Q2Ptdrsuung4AAAAAACLKaUc1lrbs+1mMAMAAAAA0MiThw4AAAAAMDHY7s1tP9lM9pOcZm3UMFy/1Kf18vJ5r/rdC/uL/hL60eV508vJ6sy9Z+9/kDw7vvlaKzlKknT7O83zbPeSTnKyOtrfH7dfyDad8eB8tzXO2LkmU5P6JMnr1Tk5pmrUennxvvPcW31m3XTj2/jyt6Xb33kUeXZX17K1+6lGeXqDL119cM53ap7rnuVnXzxdKA/NmMEMAAAAAEAjBpgBAAAAAGjEADMAAAAAAI2UWutDZ2is3W7XRdd2AQAAAABgMaWUw1pre7bdDGYAAAAAABoxwAwAAAAAQCNPHjoAAAAAwMRgu5d0Rtsnq8n+Df1Ps3a+M1xPDpLW8dkl8rzfvdB/0aU2H1ue9we9+fea3O8mB+eb/d3upcML1+dNL69XL7dfyjZtOudUntbx6HMjR+n2dxrlubY+s/eedXBxt3WcvLrr+/Omd6ntZFyvvatq1F8/q8WsjRxd2H/6YuMjnWf6eSVzvj/XGT+vq7J1knz2xdOF8tCMGcwAAAAAADRigBkAAAAAgEYMMAMAAAAA0EiptT50hsba7XZddO0bAAAAAAAWU0o5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYGKw3btVv5PN0ef+eP80a6ON/vpZn2fH5/1XcpQkefpiY+l5zrIsI8+bXl6vXm4/HW6NNg6S1vHl4xvj+93kTvXpJCfjbPuTXNO1GK6fZZzWOk46Oa9Jt79zdmzRX65fWZ+sje5/cH7PWbep0aL1eX9w9fszqU2/+6cXuua0Revz2PLc5vv1evPi/j+y+20X9qffl7vmoRkzmAEAAAAAaMQAMwAAAAAAjRhgBgAAAACgEQPMAAAAAAA0UmqtD52hsXa7XS3WDQAAAACwXKWUw1pre7Z9aTOYSymfKqX8fCnlF0spv1RK+ZFxeyml/LOllL9aSvnlUso/MdX+Y6WUD0opXy2l/N5lZQMAAAAA4O6eLPHawyTfUWv9Winlk0l+tpTyM0n+h0meJvndtdbfLqX8XeP+35XkW8b/fWuS3fEnAAAA8LeJwXYvJ5sX2/aTnGYtGa5f6t96OfrszLSv5Cjd/s6l/ov+Evox5kkneb2anA63Fjp3OuNGjuYee/piY/E8SdJJTlZHtZk4zdpoY06dkiQHl5ueHY9qlSTd/s6D1aeT8xzTFq3P20Hvwv7+Lc45q9u0/nr6u91LzYvW57Hl6X0wuNx4MHoPrjLvuczT5P2hmaUNMNfR2htfG+9+cvxfTbKV5Fmt9bfH/X5t3Od7k/z58XlvSynfXEr5dK31ry8rIwAAAAAAzS31H/krpXyilPILSX4tyZdrrT+X5O9J8odLKV8ppfxMKeVbxt1bSab/b4tfHbfNXvMHx+d+5cMPP1xmfAAAAAAArrHUAeZa62/VWteSfCbJ50opn03ydUn+5nhB6P9LkpcLXvPHa63tWmv7vffeu/fMAAAAAADcThmtSPEOblTKn0rym0l+IMl31Vr/k1JKSfLrtdaVUsq/kmS/1voXxv3/SpJvv26JjHa7Xa2lAgAAAACwXKWUw/Gk4QuWNoO5lPJeKeWbx9tfn+T3J/mPk/zbSf7+cbdOkr863t5L8o+VkW9L8hvWXwYAAAAAeLyW9o/8Jfl0ki+WUj6R0UD2l2qtP1VK+dkkr0spfzyjfwTwB8b9fzrJdyf5IKOZzt+/xGwAAAAAANzR0gaYa61fTfJ75rT/epLvmdNek/zQsvIAAAAAj9/bQS9Jsj/Tfpq1+Sf017N1fJRuf+dW1190qc3Hlmew3bvy2O7mWlq760mSjRwtdN2Jpy827panMyfX6nmuWZPuK3Pydvs7S6tP54p73mTh+ry5mOdk9eZz9q56t5K0dtfPnm2j+txnnuF6Wi9zr3lu8nqc9+z7119P6/j8+OxzXfR50cxS/5E/AAAAAAA+vgwwAwAAAADQiAFmAAAAAAAaKaOljz+a2u12XXRtFwAAAAAAFlNKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBisN278tjJ5uW2/SSnWbvYOFxPkrReJp0kz/vds0OL/hL6yjyd5GT1YfPMq8dNGeZpvUw2cpRuf+du9ZmqydwMkxwH4/seXzzUmen6vN+9tzzT5ma7qj4Ho5z3Up95OslvfHlroWuujLMk9/g+N8yzkqMkub88nWR3de3a9/VKB0l/t3uhycoH74YZzAAAAAAANGKAGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrhbrBgAAAABYrlLKYa21PdtuBjMAAAAAAI08eegAAAAAABOD7d7Fhk5ysprsZe28rb9+tvns+OprPe93L7Ut+kvo2Twnm6PPRfOs5Cjd/s6950lnnGt19Lmf5HS4daFL6+XFUzZyNPfa3f5O8zyd87ZJlkmeJDnNWjI8r1MOktacWk2yTWr1EPXpZPS8Zt2pPnMy3ejL33bloXurzyPMM/mO7Sf5R3avvudVGaZZ+eDdMIMZAAAAAIBGDDADAAAAANCIAWYAAAAAABoptdaHztBYu92u1lIBAAAAAFiuUsphrbU9224GMwAAAAAAjRhgBgAAAACgkScPHQAAAABgYrDdu7B/sjm/3/748zRryXA9ORjtt44v9uskWclRuv2dJMmiS20+ujxvejlZvb7PhSzXaO2uX8r19MXGYnkm9emct03y7U/1O6vLxMF5bSanruTowrW7/Z2l1ucs1zzD9bRenu82rc/7g961x0+HW9cen86QJBvjGjV9fx5bnrdX5Nm/xbk3PbuNBs+LZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m6366JruwAAAAAAsJhSymGttT3bbgYzAAAAAACNPHnoAAAAAAATg+1eTjZH2/vjttOsjTaG65dPOLjc9Ox49Pm83710bNFfQt81T+s46eT+8rwd9HK4u7XQOUmykaMb+3T7O43qc2b8h56sjj73sjaq0cGoDtM6U9srM9m6/Z2z7XdRn9vUZpKrSZ5kzrszMa7PtGcztbrOZ188/cjn2eufP6+t49s9i9to8rxoxgxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGjEADMAAAAAAI2UWutDZ2is3W5Xi3UDAAAAACxXKeWw1tqebTeDGQAAAACARp48dAAAAACAicF2L+lcbDtZHX3uZW20MVxPDi72eXZ8vr2So3T7O3Ovv+gvoR9dnje9ue2vV68/73S4dSnjtP5u9055JjXZv+7+V5nKtXV8sVb3UZ+T1atzXci3hPq8HVzOMy/LhfpckWPr+OjCfre/85HP8/6cPGcZJt+v/vpC15zYOj7K0xcbjc5lMWYwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjpdb60Bkaa7fbddG1XQAAAAAAWEwp5bDW2p5tN4MZAAAAAIBGnjx0AAAAAICJwXYv6SQnq6P9/SSnWRvtDNfz7OXtr7WSoyRJt79z1rboL6HvO890lrvkmTXJd1t7WUv662kdJxtTuRrlSS5kuk2W/fHnadaS4fpo5yD3k2emPovWZpLvtL915zy9wZdGG/31hc5rHV9//FW/+7HIc/b+TDnZXOgSSZK9/laSpL/bvdBu5YN3wwxmAAAAAAAaMcAMAAAAAEAjBpgBAAAAAGjEADMAAAAAAI2UWutDZ2is3W5Xi3UDAAAAACxXKeWw1tqebTeDGQAAAACARp48dAAAAACAicF272JDJ9ldXUtrd322OSs5utU1u/2ds+1Ffwn96PK86V157De+vHWp7baZJp6+2Fgsz2x9pnXON09Wk72c121yaF6+ZdTnZDX5ht2L9Vm0Nsni9Xk76GV/4bucO81a0l9P6/hi+6t+N8ni9Xlsec7en871/eZ5vXpx//Pj5/t8nKVJHpoxgxkAAAAAgEYMMAMAAAAA0IgBZgAAAAAAGjHADAAAAABAI6XW+tAZGmu329Vi3QAAAAAAy1VKOay1tmfbzWAGAAAAAKCRJw8dAAAAAGBisN0bbXTO205Wz7f3spYM15OD87Znx+fbKzlKt79z5fUX/SX0rfL01y+cs8w87w96OR1uXWw8SFrH8/tPdMZZbvL0xcZCeabrc7Ka7Cc5nXpG07lumyHJWc0WrU9v8KXRvSfusTbJ4vXpDb506f24yk05p73qd5M0rM8jyvP+oHdh/7S/dUXP22eafFWf97sL56EZM5gBAAAAAGjEADMAAAAAAI0YYAYAAAAAoJFSa33oDI212+1qLRUAAAAAgOUqpRzWWtuz7UubwVxK+VQp5edLKb9YSvmlUsqPjNv/r6WU/6SU8gvj/9bG7aWU8mOllA9KKV8tpfzeZWUDAAAAAODunizx2sMk31Fr/Vop5ZNJfraU8jPjY/+7WutPzvT/riTfMv7vW5Psjj8BAACAv00MtntJ53x/d3Ut6a/n2XGykqMkSbe/0/j6i/4S+rHleX/Qu7B/mrVkuL7wfVsvz7df9buN8wy2x3mmanSymuyNcz17Ofe0CyZ1TC7XctE8bwe97E/tP4r6dG7udxu7q2ujjf56to6P0u3vfOTzTL/Pp8OtRjmuelbJ4s+LZpY2g7mOfG28+8nxf9etx/G9Sf78+Ly3Sb65lPLpZeUDAAAAAOBulvqP/JVSPlFK+YUkv5bky7XWnxsf+mfHy2D8mVLK143bWkkGU6f/6rgNAAAAAIBHaKkDzLXW36q1riX5TJLPlVI+m+SfSfK7k/zPkvx3k/xTi1yzlPKDpZSvlFK+8uGHH953ZAAAAAAAbmmpA8wTtdZfT/ImyT9Qa/3r42UwhkleJfncuNtJkqdTp31m3DZ7rR+vtbZrre333ntvyckBAAAAALhKqfW6ZZHvcOFS3kvy39Raf72U8vVJ+kl+NMlhrfWvl1JKkj+T5G/WWv/pUsr3JPnhJN+d0T/u92O11s9ddf0kabfb1WLdAAAAAADLVUo5rLW2Z9ufLPGen07yxVLKJzKaKf2lWutPlVL+8njwuST5hSR/dNz/pzMaXP4gyW8m+f4lZgMAAAAA4I6WNsBca/1qkt8zp/07ruhfk/zQsvIAAAAAj99gu3e+0xl97P7KVpKkdXzx0EqOLpzb7e/ceP1Ffwk9m+dkNdnrP4I84yxX2Z/aPs1aMly/cHzr5XnW6Zx3yTPP69XLGVovz0+Zrdl95rmqPvtT2zfV5j7yvN682PaNu1vXnnNFKee+Xx+HPLNONud0nLKXtbnt/e6fvtRm5YN3452swQwAAAAAwMePAWYAAAAAABoxwAwAAAAAQCMGmAEAAAAAaKSM/m29j6Z2u10t1g0AAAAAsFyllMNaa3u23QxmAAAAAAAaefLQAQAAAAAmBtu9G/ucbCb74+3TrCXD9QvHWy+Tznj7eb974diiv4S+MU8nOVl9d3neDi7m2cvaxQ7je/e/cPE+t9WoPp3L7SerV5+zn3GdklHeg6R1nGzk6KxPt7/TKM9sfZI5NRrft0mN7pLncHfrwrHpv3dR91Gfx5ZnnrnP7ipT37vWy9Hf8/TFxkJ5aMYMZgAAAAAAGjHADAAAAABAIwaYAQAAAABopNRaHzpDY+12uy66tgsAAAAAAIsppRzWWtuz7WYwAwAAAADQyJOHDgAAAAAwMdjuJZ3k9WpymrWkv54kaR2f9+mMP1dyNPca3f7Olddf9JfQkzy7q2ujhseQp6lOcrKafMPu1tzDKznK0xcb95unc755sjr63M/o2bZ21y90mdRvul7vtD7jMK9Xk8/PqdFd6rP7fTPXO7j4Dt3Wq373wn7T+jy2PCeb1/fbm/rfgq3j+d+z5PJ3zcoH74YZzAAAAAAANGKAGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrhbrBgAAAABYrlLKYa21PdtuBjMAAAAAAI08eegAAAAAABOD7V5ONkfb+zPHTrN29YnD9Yv7Bxd3W8fJq343i/4S+tHledPLyer5/n6S0+HW3Htc59lxspKjC23d/k6j+kxcVadMZ5x2MKpDZ6Z5JUfp9neS5E7Pa2J/9v4L1Gnr+LxGd63P683xO9Nfv/qEJP3d7q2vf+c8w60b67HMPG8HvSuP7Wd+vaafyVWavj80YwYzAAAAAACNGGAGAAAAAKARA8wAAAAAADRigBkAAAAAgEZKrfWhMzTWbrerxboBAAAAAJarlHJYa23PtpvBDAAAAABAI08eOgAAAADAxGC7N7f9ZPN8ez/JadbOG4brycH867WOk06S5/1ukmTRX0I/ujxvLuc5WU32hlvz7/fy6mtt5OhS29MXG4vl2e6N/qBxjmRUj4nTrI3qMevgvBYrc3J0+ztJ7qc+r1eT0wXrM682yeL16X0wGG1c8T7cxtbx/dXnseWZ97wmXq9e3D8dbp09r86c/vPeo0WfF82YwQwAAAAAQCMGmAEAAAAAaMQAMwAAAAAAjZRa60NnaKzdbtdF13YBAAAAAGAxpZTDWmt7tt0MZgAAAAAAGnny0AEAAAAAJgbbvbntJ5ujz/0kp1k7PzBcT+vlaLMzc85Kji7sd/s7WfSX0B+JPJ3kZHWcZbh13n5w9XVax6PPjRyl2985a2+UZ+r+VznNWjJcn39wKmfrOHnV794tz7RO8nr1mgxX1Oje6vNm/vszcbJ68zX2M5N9nPnZcfLZF08/0nneH/Sufzdu62D0zGa/c4vmoRkzmAEAAAAAaMQAMwAAAAAAjRhgBgAAAACgEQPMAAAAAAA0UmqtD52hsXa7XRddXB0AAAAAgMWUUg5rre3ZdjOYAQAAAABo5MlDBwAAAACYGGz35rafbCZ7WRvt9NfTOr75Wp0kz/vdC22L/hL6seY52Uz2k5wOt64/4eB8czbjRo7Otrv9nTvlSSc5WR1lyrxcBxfv35k6tDLOMckwrUmek83R9t5NtRnnmriqPtO5muR5Pc5zmrVkuH52z9n7dbKYlRzl6YuNR5Pneb+7cH3eDnq3e49vqfXy4nOz8sG7YQYzAAAAAACNGGAGAAAAAKARA8wAAAAAADRigBkAAAAAgEZKrfWhMzTWbrerxboBAAAAAJarlHJYa23PtpvBDAAAAABAI08eOgAAAADAxGC7l3SSk9VkP8lp1kYHhusXOx6cb7aOL1+nM7X9vN892170l9CPMs8tnWxebtvL2uXsSVovk1f9bvM84z/wZPWKe06bvv/Beb06SVZydHao299ZWn3m1WY/yelwa27/+6jP6zm1mXXp/te8Vx+HPG8Hl5/X3uSeB8mzOd+lRXz2xdO7XYBbMYMZAAAAAIBGDDADAAAAANCIAWYAAAAAABoptdaHztBYu92ui67tAgAAAADAYkoph7XW9my7GcwAAAAAADTy5KEDAAAAAEwMtnvnO53kZHW0uZ/kNGvzTxqujz4PktbxhdOzkqMkSbe/kyRZ9JfQjy7Pm95Zhnn2rsrUXz/bbB3Pz9Ioz3ZvdLE5Xq9O1Wh8/0k9pu8/m2HafdZnP1c8s1vWplGe6ffnJlfUMUn2vrx16fBKjvL0xcajyfO8372/+kx91/Zm3qHk4vdqI0f39v7QjBnMAAAAAAA0YoAZAAAAAIBGDDADAAAAANCIAWYAAAAAABoptdaHztBYu92uFusGAAAAAFiuUsphrbU92760GcyllE+VUn6+lPKLpZRfKqX8yMzxHyulfG1q/+tKKX+plPJBKeXnSim/a1nZAAAAAAC4uydLvPYwyXfUWr9WSvlkkp8tpfxMrfVtKaWd5L8z038zyd+ota6WUr6Q5EeT/OEl5gMAAAAemcGbXl6vjrZPs3bx4HB99HlwsfnZ8cX9lRydbXf7OxeOLfpL6EeXZ7uX15uX20+zdp7nNsaZnx0nz/vdO+WZdjLOtp859ZoYrl+q2URrXLtORrka5enk7JlNnGW5TY1msrWOk1fjGi2a5/1B7+ZOY6fDrRv79L/QvbD/Uc8z/f683lzwOY21Xo4+X/W7l45Z+eDdWNoM5joymaH8yfF/tZTyiST/XJI/OXPK9yb54nj7J5N8ZymlLCsfAAAAAAB3s9R/5K+U8olSyi8k+bUkX661/lySH06yV2v96zPdW0kGSVJr/VtJfiPJ75hzzR8spXyllPKVDz/8cJnxAQAAAAC4xlIHmGutv1VrXUvymSSfK6X8fUn+YJJ/8Q7X/PFaa7vW2n7vvffuKSkAAAAAAIta6gDzRK3115O8SfL3J1lN8kEp5a8l+W+XUj4YdztJ8jRJSilPkqwk+S/eRT4AAAAAABZXaq3LuXAp7yX5b2qtv15K+fok/SQ/Wmv9qak+X6u1fsN4+4eS/L211j86/kf+/kCt9Q9dd492u10t1g0AAAAAsFyllMNaa3u2/ckS7/npJF8c/6N+f0eSL00PLs/xE0n+tfGM5v8yyReWmA0AAAAAgDta2gBzrfWrSX7PDX2+YWr7b2a0PjMAAADwt6nBdu9iQyc5Wb3YtJ/kNGujneH66PPg/Hjr+OzUrOTowrlPX2x8pPO8P+id3+s6/fVbXa+/272wv+gvxQfbvZxsjrb3p9qvzXhDttZx8qrfbZTnvuuTXKzRonl6PzGY2946TjZm3oV5uv2da49/1PO8P+hde/zKZzlcv/Adm6e/2104D828kzWYAQAAAAD4+DHADAAAAABAIwaYAQAAAABopNRaHzpDY+12u1pLBQAAAABguUoph7XW9my7GcwAAAAAADTy5KEDAAAAAEy8HfTOtvdv6HuatYsNw/X5HQ+S1vFo84dePP1I5xm86V1qe71683kXsg3XL2SY6CT57KJ5tnujE5OcrJ7X6FItkqR/sR6T+3eSrOQo3f7OpVMW/eV6k/pcyjrOeS/1mZNn1nTdrnM63Lqw3/9C917q85jz3ObdnnU63ErrZfKqv3gemjGDGQAAAACARgwwAwAAAADQiAFmAAAAAAAaMcAMAAAAAEAjpdb60Bkaa7fb1WLdAAAAAADLVUo5rLW2Z9vNYAYAAAAAoJEnDx0AAAAAYOLtoJf9Oe2nWUuG66Odg8Wu2d/tnm0v+kvox5ZnsN3LyWayl7Vb3Gj9ykOt46Qztf+8322c50wnOVm93Gd//Hk6yTycyTVVv9bx6PPVHfLcuj7D9Wuf3XSN7lKfk82Lbfvjz7N3aE6GSR0mOlMZpj1Uno0cpdvfuXOet4PR+7PX37qx77Pj+e0rOZrb3u3vLJyHZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGDDADAAAAANBIqbU+dIbG2u12tVg3AAAAAMBylVIOa63t2XYzmAEAAAAAaOTJQwcAAAAAmBhs9/J683z/dLh1scPB+WbrOOmMt5/3u7e6/qK/hH6MeW5rd3Mtrd31s0wTKzm61Lfb37n/PJ3kZDXZy9pof7ie1stLXS5lmmS59zwzdr9vK62XuVCfebWZztQoz+wDGDtZvbi/l7Vs7X7q1tfu9nc+8nn+XG9wVZwbXfWs7pKHZsxgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m63q7VUAAAAAACWq5RyWGttz7abwQwAAAAAQCNPHjoAAAAAwMRgu3djn5PN0ede1pL+elrH8/t1kqzkKN3+zlnbor+EfrR5OuN7ry50+pn9JKfDrdHOQdI6Tl71u3evT+d8c5Jtf7x/Oq7PtEmtpk67UKMHrU/WkuF6Wi9HbY3q86aX3adb8w8ejD6eXfG+XOd5v5ukQX0eW54536/J9+m29sbvcevl6BlNs/LBu2EGMwAAAAAAjRhgBgAAAACgEQPMAAAAAAA0YoAZAAAAAIBGSq31oTM01m63q8W6AQAAAACWq5RyWGttz7abwQwAAAAAQCNPHjoAAAAAwMTbQS/7c9pPs3a+M1y/ePDgdtfu73az6C+hH1uewXYv6SQnq6P9vekcN2jtrl9q28jR2Xa3v9M4TzLKtD9uP53NNVWj1sv51+okWclRuv2ds7aF87zpnWVJbl+febWZmNSoaX1eb462L9XkNqbfrZn3qun789jyTL/Pt3XVc23trudVv3u2b+WDd8MMZgAAAAAAGjHADAAAAABAIwaYAQAAAABoxAAzAAAAAACNlFrrQ2dorN1uV4t1AwAAAAAsVynlsNbanm03gxkAAAAAgEaePHQAAAAAgInBdi/pnO+frI4+96855/O7W1nJUZKk29+59vqL/hL6UeZJks55lll7/a0kydbx0a2vO8l5lzzJKNN+ktOsJcP15CBpHZ/37yRntZm+71Xuuz5NapPcrT67m2vX9mntri90zYlX/e5HP8+b3pXv8aL2spb018+ebbe/s3AemjGDGQAAAACARgwwAwAAAADQiAFmAAAAAAAaKbXWh87QWLvdrtZSAQAAAABYrlLKYa21PdtuBjMAAAAAAI08eegAAAAAABOD7d61x082R5/7c46dZm200V8/a3t2nKzkKEnS7e9k0V9CP8o8ndv1PVm93Dad8zRryXA9ORjtbx0f5emLjUZ5pu81ucfZ35+M7jNtfM/W8cU/ZyVH6fZ3zvaXUZ+b6pJcrk2S9He7zd+fmUzzMlxnr7+V1vH5/sa4Tg+V53B368J+4zxvrv9+3eSq3PtJfrLbXzgPzZjBDAAAAABAIwaYAQAAAABoxAAzAAAAAACNGGAGAAAAAKCRUmt96AyNtdvtarFuAAAAAIDlKqUc1lrbs+1mMAMAAAAA0MiThw4AAAAAMDHY7p3vdM43T1aTvawlw/XzxoPL57eOL5yWlRydbXf7O1n0l9CPOs9NOqOcE/szh0+HW2eZt46P7j/P+A+/McPEOMuz4+R5v5skj6Y+/d17zjNz7+vsZS397p+ee+xjkadzc79Zr8dZT/tbl449Ox59z5q8zzRjBjMAAAAAAI0YYAYAAAAAoBEDzAAAAAAANFJqrQ+dobF2u12tpQIAAAAAsFyllMNaa3u23QxmAAAAAAAaWdoAcynlU6WUny+l/GIp5ZdKKT8ybv+JcdtXSyk/WUr5hnH715VS/lIp5YNSys+VUn7XsrIBAAAAAHB3T5Z47WGS76i1fq2U8skkP1tK+Zkkf7zWepokpZR/IckPJ3k/yWaSv1FrXS2lfCHJjyb5w0vMBwAAADwyg+1ekuRkM9mfaj/NWjJcv3zCQdI6Pt/tjD+f97tzr7/oUpuPMc/rzeR0uJUcjNom9+tM9VvJ0a2u1+3v3DnP7M1fr55vn9XpYH7O2az3keex1Wd3c+38XTm4uu+z48VzfdTzvD/oXXv8dLiVZy/P95edh2aWNoO5jnxtvPvJ8X91anC5JPn6JJNFoL83yRfH2z+Z5DvHfQAAAAAAeISWugZzKeUTpZRfSPJrSb5ca/25cfurJP9Zkt+d5F8cd28lGSRJrfVvJfmNJL9jzjV/sJTylVLKVz788MNlxgcAAAAA4BpLHWCutf5WrXUtyWeSfK6U8tlx+/cn+buT/HIWXAaj1vrjtdZ2rbX93nvv3XdkAAAAAABuqdRab+51Hzcq5U8l+c1a6z8/1fb3JfmTtdbPl1L+vSTbtdb/oJTyJKMZzu/VawK22+1qLRUAAAAAgOUqpRzWWtuz7UubwVxKea+U8s3j7a9P8vuT/JVSyuq4rSTZSPIfj0/ZS/JHxtv/cJK/fN3gMgAAAAAAD+vJEq/96SRfLKV8IqOB7C8l+XeS/PullG9K/v/s/X+QZP1+H/S9v/YKRKLrccAPILfWJRcjVcq+jkdOW9bgkCFOgrFxjapJAZcFw5ohIsN1kMAhVe5UYDyJlgeDcVAlTFDQbmxqsbmFnNRGEbivqZ0hSvmRcveOLFuSVdyRTUaDKr62scbGcYOUb/7o7pnTPd0z02emd2YfvV5VT51zvufXez/ne3qe+tbp0ylJ/lSS3fH235fk3y2lfCXJX07yuRVmAwAAAB6h07f9nK0nh+Pl82xcrhxuJkk6L6/ut5VkLcdzj9kb7F/ML/tN6MeW5+PT/sX8+WD3Yr5zstRhkiTbOZ7K0ibP6dtRnkmNzrNxUZccXZ9tXo3ummdSn/Ph7o3nv8mrQe9K29L12etfbdy6fp+z9ZuP2zn4tvQG+x98nmZ/XuQ8G8lg8/Jct7yerwa9pfPQzsoGmGutP5bkW+as+s0Ltv8bSf6hVeUBAAAAAOB+rfRH/gAAAAAA+PQywAwAAAAAQCsGmAEAAAAAaKXUWh86Q2vdbrd6WTcAAAAAwGqVUt7VWruz7Z5gBgAAAACglScPHQAAAABg4nSvP92wlZytJ4eNpvNsJMPN5CjpnExtesVajqeWn77Y/rDzvO1fu/5sfTQ9nGk/H+zm2cns1snzQW9qedlvil/Up/GPbWY4z8ZoYVyfic7JaJfZejT1BvvL57mhPpN8hzNt54Pdi0xNazlOb7B/sbxsno9P5+c5H+5OLXde3lyPWW3q89jy3OZ6zXq9vnhds5+v5Xjp+4t2PMEMAAAAAEArBpgBAAAAAGjFADMAAAAAAK2UWutDZ2it2+3WZd/tAgAAAADAckop72qt3dl2TzADAAAAANCKAWYAAAAAAFp58tABAAAAACY+Oe3ncDx/no1kuDlaOLq67bOT5Pmgt9Txl33V5mPLc7rXn1o+27mc/7qD3aWONev5oHfnPEmSrcvZs/Wrqw8zrmUyque4lp2T6d3XcpynL7bvlKdZnzeD3Tw7WepwU+6jPmc7yZvJv31i0qeWcZQMDh5Pns7L5NU99Z/mNUuW79fNe9Crdd8PTzADAAAAANCKAWYAAAAAAFoxwAwAAAAAQCul1vrQGVrrdrvVu1QAAAAAAFarlPKu1tqdbfcEMwAAAAAArTx56AAAAAAAE6d7/dHM1mjyen00Pc/G9IbDzSRJ5+XU5lPWcnwx3xvsJ0mW/Sb0Y8xztpMczrSfD3evbnx0/bE6J6Ppq0Hvom3pPG/7OVufk6dZn+Hm3CyT8yejej1v5GidZ6+f1zszWe5Qm9lcbeoz6TPJzXVp1mTWvBp9mvLcdJ2uy7Io32dfPF1uJ1rxBDMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVkqt9aEztNbtduuyLw8HAAAAAGA5pZR3tdbubLsnmAEAAAAAaOXJQwcAAAAAmDjd61/Mn+1cXX+Y5Dwbo4XhZnKUdE4WH28ryfNB72J52W9CP7o8b0d5Xq+Pli/O3TTcXLh/5+UoQ2ZytM7TqE+2krP1UU1mnWfjMte4RltztlvLcZKkN9hvl2emPhfnbrqmPs9eTi/P1mjZPP2vnC5ct/vy+Erb5N99W6vMs2yWNnk+OR1dr8Olz7Sg7ycX9+HuyXGevthucWSW5QlmAAAAAABaMcAMAAAAAEArBpgBAAAAAGil1FofOkNr3W63LvtuFwAAAAAAllNKeVdr7c62e4IZAAAAAIBWDDADAAAAANDKk4cOAAAAADBxutcfzWxNt79ev3nf8+HuaOYo6ZyMDrGW4yRJb7CfJFn2VZuPMc/rndGxOy+T7fHx7urO9UmmanS2nhwmOc9GMtxMknReTu/brMd95nlM9fn4dFSfi76wwGxt5pn9t/QG+5+KPOfD3ey+vJ/r1MySLH+9aMcTzAAAAAAAtGKAGQAAAACAVgwwAwAAAADQSqm1PnSG1rrdbvUuFQAAAACA1SqlvKu1dmfbPcEMAAAAAEArTx46AAAAAMDE6V4/2ZpuO1tPDsfz59lIhpuXK4+SzslotrnbWo6njtEb7CdJlv0m9KPL87af1+vz151nIxmMskwy3GSS8fmg1y7PXv/iQHPrcjQ/y1au1iS5rMvEKupz29oklznvcr3mefPF3aWOs92oVbNGD5Fne0FfbpPnk9P5eQ5v2G/RtZy9556+2F4qD+14ghkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u3XZl4cDAAAAALCcUsq7Wmt3tt0TzAAAAAAAtPLkoQMAAAAATJy+7U8tv15Pzoe7ydFouXMymm7nOEnSG+wvdfxlvwn9GPOcrc9f9yYbi3ccbF5kXeTVoLd8nr1+znYulw+TnM/LMdy8qNmsebleDXpJ7q8+C3M1sq2kPjP9Z+JgfX6WZwebeT7+t9/Gh57nk9PLPHP773Bz7n6dl7c7/udfPF0qD+14ghkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWim11ofO0Fq3263LvtsFAAAAAIDllFLe1Vq7s+2eYAYAAAAAoBUDzAAAAAAAtPLkoQMAAAAATJzu9UczW6PJ2fpoepjkfLibHI2WOydTm+X5oHer4y/7qs3b5JlkmWy2luP0BvurzXONs53p5cMk59lIhptTebdm9ns+6N09z5w6NZ1nYzQz3LxsnJNpUsN7r8/WZbaJwzTq08hzb/UZH+j1+vXbNvv3TXZPWtbn7WV9rstzPtwdzaw4zyenN/fned40rtezl4u3++yLp62Oz3I8wQwAAAAAQCsGmAEAAAAAaMUAMwAAAAAArZRa60NnaK3b7dZl3+0CAAAAAMBySinvaq3d2faVPcFcSvnaUsqPlFL+VCnlx0spv2/c/rqU8lOllD9TSnlZSvmacXsppXxPKeUrpZQfK6X8hlVlAwAAAADg7p6s8NjDJL+l1vrXxoPIP1RK+Q+TvE7yj4+3+feS/NNJDpL8tiTfNP7vN43bftMK8wEAAACPzCen/STJYZLz4e78jY6Szsnl4qtB79bHX/ab0JM8b7KRDDcfPM/p3ihPtqbbz9aTrzu4Wq+1HN/quL3B/r3mmfV6PTnPRjoHm1c2nZfxTnkWZHnzxd25q25To7vkeb1z++2v9PmZvpVM96/3nafz8uo2d8rztp+z9cXrD8fTz8zp2/NMru9ajtMb7C+dh3ZW9gRzHflr48WvGf9Xa60/OF5Xk/xIkm8Yb/PtSf7weNUnSX55KeXrV5UPAAAAAIC7WemP/JVSfmkp5UeT/IUkX6y1/nBj3dck+Z1J/qNxUyfJaWP3nxm3AQAAAADwCK10gLnW+gu11o2MnlL+1lLKZxur/60k/0mt9f+xzDFLKd9RSvlSKeVLX/3qV+8xLQAAAAAAy1jpAPNErfWvJHmb5O9PklLKv5zkoyT/QmOzsyRPG8vfMG6bPdb31lq7tdbuRx99tLLMAAAAAABcr4xehbyCA5fyUZL/utb6V0opf0uSQZJ/NcnfmeSfSvI/rLX+fxvb/wNJfneS357Rj/t9T631W687R7fbrV7WDQAAAACwWqWUd7XW7mz7kxWe8+uT/KFSyi/N6EnpL9Raf6CU8vNJ/rMkf7KUkiR/rNa6n+QHMxpc/kqSv57kd60wGwAAAAAAd7SyAeZa648l+ZY57XPPWUePUn9+VXkAAACAx++T034Ok5xnIxluXr/x0eVs5yR5NejdePxlvwn92PKcvu3nYH1j/srBDfkaOidX214Nesvn2evPX7E1mpytJ2+ycdk+3Ezn5cLNs5bji7beYL9Vntc74+s1zy1rdF/1mfSfpoXZmm7I+ewkef4pyfPmNue/Qefgar4214t23ss7mAEAAAAA+PQxwAwAAAAAQCsGmAEAAAAAaKWMXn38Yep2u9W7VAAAAAAAVquU8q7W2p1t9wQzAAAAAACtGGAGAAAAAKCVJw8dAAAAAGDik9N+Dmfazoe7N+94tHjVs5Pk+aCXJFn2VZut8lyT5a55Tvf6eb2TnA+uz7B7cnylrTfYv/H4bfIstJWcrV8uvsnGaGawuXCXZyfJWo4vsrbNc7Czce15JmbrdFON2uQ521m8/usORtdxLVev16x52T70PP3vO11q+6Z5fXzW0xfbrY/P7XmCGQAAAACAVgwwAwAAAADQigFmAAAAAABaKbXWh87QWrfbrcu+2wUAAAAAgOWUUt7VWruz7Z5gBgAAAACglScPHQAAAABg4nSvf+36s53kcKbtfLibzstka872azlOkvQG+0mSZb8J/ZjznO0stWuS5E02LheGm+m8HM1uJXk+6LXKM5vjMMl58zyN8+VoNNs5mX+8rYxqdNf63Lk2SToHm1O52tbnujyHjfkrNRtuZqGj5NlJ8tkXTz8VeSauu26z12eieZ1eDXpT67z54P3wBDMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVkqt9aEztNbtdquXdQMAAAAArFYp5V2ttTvb7glmAAAAAABaefLQAQAAAAAmTvf6lwtbl7Ov10fT82zM33G4mRwlnZPpXddynN5g/2KzZb8JfR95GrvdPc/byzwH6xuj8yTpvFzqMElGudZyfLHcG+y3q8/4H/h6vVGP4ebcTLPnbGrWZaJtfVZRm1Z59vp5vTOaPx/uXq5o9NV5577OJFfb6/WY8nxy2s+b5j002LyYXZTnJq8GvYt5bz54PzzBDAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvtVu9SAQAAAABYrVLKu1prd7bdE8wAAAAAALRigBkAAAAAgFaePHQAAAAAgInTt/28Xr9cPs9GMtzMs5e3P8ZajtMb7M9dt+yrNh9bnk9O+3kz2F24vnNy/f6vBr1r17epT5KcrSeHGdcnSYabo+nR/P3m5dzO8cX8pF6t6jPcXXjeReduuq5GS9dnr5+zndtv/2ZSvxm7B187tdy2Po8tT//7Tpfavum66zi5hl6t+354ghkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWim11ofO0Fq3263epQIAAAAAsFqllHe11u5suyeYAQAAAABo5clDBwAAAACYON3rL165NZqcrc9ffTienmcjGW4mR6Plzslo1+eDXpb9JvRjy/Pxaf/yeDc5Gp1rGZ9/8XSp7U/f9nO2Pvq3nmdj1DibbU6O7RxPLfcG+3OPv2x9+qdfmJ9hVuNa3Narlv3n9c7121zU7TYa/Whw8OHn6X/ldPHKo6tNq75etOMJZgAAAAAAWjHADAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvtVi/rBgAAAABYrVLKu1prd7bdE8wAAAAAALTy5KEDAAAAAEycvu3nbH00fzhuO8/G4h2Gm6Pp0WjSORlNt+Zs+nzQy7LfhH5seT457S+1fZK8yUYy2Lxxu8FBi/rs9XO2s3j9YRbUa7h5UaOJzsmoTs8HvYu2D74+b/s5WN+47BcNuy+PlzrWRG+wfzH/oee56Xrd9tos8mLnaet9uT1PMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u3XZd7sAAAAAALCcUsq7Wmt3tt0TzAAAAAAAtGKAGQAAAACAVp48dAAAAACAidO9frJ1tf31enI+3E2OLtuenUxvs5bj9Ab71x5/2VdtPso884wznq2PpoeNVefZuLr9YPNitpn7sy+e3k+eJGc708uHkyzD0bk7L+eWNsll7e6tPkmydVmfSZ7k+vrMXtNl6/Px6TV5Jucf7o5mjq7frmlw0EuyfP95bHlO307neb1+9Xo8O9hMG88HvaXz0I4nmAEAAAAAaMUAMwAAAAAArRhgBgAAAACglVJrfegMrXW73epdKgAAAAAAq1VKeVdr7c62e4IZAAAAAIBWnjx0AAAAAICJ073+1cat0eRsPXmTjWS4mWcvlzvuWo6TJE9fbH/weQ6e7iZJOidTcW7l+aB37fplvyl+XX1er4+m58PddF7eLuekLr3Bfus8zfosU5uJ62p0L/W5zlbyc1/cvXaTZo0+9Dwfn/ZzPmjfn6/L1CYP7XiCGQAAAACAVgwwAwAAAADQigFmAAAAAABaMcAMAAAAAEArpdb60Bla63a71cu6AQAAAABWq5TyrtbanW1f2RPMpZSvLaX8SCnlT5VSfryU8vvG7b+7lPKVUkotpfyKxvallPI943U/Vkr5DavKBgAAAADA3T1Z4bGHSX5LrfWvlVK+JskPlVL+wyT/zyQ/kORwZvvfluSbxv/9piQH4ykAAADwi8TpXj/ZWrz+4Kd38+zkavtajudu3xvsTy0v+03oR5fnbT9JcrY+Wj5Mcj7cXeoYuy+Pr+RonWevf7kwU6ez9XG+bCTDzSv7dl6OptvjWs3L1LY+U+dfoj6dl8mrQW/h+jZ5Xo+v1Xk2ps91MF2T6877qc3T7D/X2brs828WXM/B567m9eaD92NlA8x19O6NvzZe/Jrxf7XWepwkpZTZXb49yR8e7/dJKeWXl1K+vtb6s6vKCAAAAABAeyv9kb9Syi8tpfxokr+Q5Iu11h++ZvNOktPG8s+M22aP+R2llC+VUr701a9+9V7zAgAAAABweysdYK61/kKtdSPJNyT51lLKZ+/hmN9ba+3WWrsfffTRnTMCAAAAANBOGb2R4j2cqJR/Kclfr7X+6+PlP5+kW2v9i+PlfzvJYa31j4yXfyrJ33vdKzK63W71LhUAAAAAgNUqpbyrtXZn21f2BHMp5aNSyi8fz/8tSf7HSf7sNbu8SfJPlJFvS/Jz3r8MAAAAAPB4rfIVGV+f5G0p5ceS/L8yegfzD5RS/rlSys9k9NqMHyul/Dvj7X8wyU8n+UqS/1OSf3aF2QAAAAAAuKP39oqMVfCKDAAAAPh0Od3rz1+xdTl7tp4cjuc/c7A7tXotx0mS3mB/7mGWHUd4dHneLsjTyDJxmOQ8G6OF4eZoenS5fvfk+Mr+T19s31ueSV3OszE6//jcnZPRdDtXzz8xqdd912eSK7m+Pp2Tq/l6g/2l83x82r88xy10DjZv3ObVoHcxv2ye/ukXltp+1Xlue73ezKvhcH62zsvL+c+/eLpUHq733l+RAQAAAADAp5sBZgAAAAAAWjHADAAAAABAK97BDAAAAADAtbyDGQAAAACAe/XkoQMAAAAATJzu9Rev3BpNztYvm95k43JhsJnOydXdtnOc3mA/SbLsN6EfW56PT/s5H+7OXdd5OTp2W73Bfrv6bI1qcDhuO5/UYLh5kWvWVpK1RtZJPWZ9WuozManTbI1u7SjpnCSvBr0kLfvzPeaZXNvWed5e3l8HT3ez+7L99ZnV5nrRjieYAQAAAABoxQAzAAAAAACtGGAGAAAAAKAVA8wAAAAAALRSaq0PnaG1brdbvawbAAAAAGC1Sinvaq3d2XZPMAMAAAAA0MqThw4AAAAAMHG6179xm7Odm4/zZrA7tdw5SV4Neln2m9CPNs9WcraevMlGOgeb1+6zneOL+d5g/9pt71Sfrel1k3wZTOfrnExvt5VkrZGxmfMu9bnIMNxN5+X1+922RnfNc52z9cXr3mQjuwdfO9XWG+x/evI0z3uL+ykZXdccTfenV4Pe1DbefPB+eIIZAAAAAIBWDDADAAAAANCKAWYAAAAAAFoptdaHztBat9ut3qUCAAAAALBapZR3tdbubLsnmAEAAAAAaMUAMwAAAAAArTx56AAAAAAAE6d7/ZztjObfZGN65WAzSdI5GS1uzey7luP0BvvXHn/ZV23eNs9slpXledvPwfrG1RXDzeTosjZN2zm+9pjNjG3yTJytJ4dJzmfrNMmXJEfXH29Sy7Vx5qcvtpfOM7c+kwx3rNFd63PRhyb1uEHn5fz2V4PepyLPx6f9UX+ZPf+C67Ssz794eveDcCNPMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u3XZd7sAAAAAALCcUsq7Wmt3tt0TzAAAAAAAtPLkoQMAAAAATJzu9UczW6PJ2XpymOQ8G5cbDTeTo9Fs5yTZzvHcY/UG+1falv0m9GPL88lp/+r5bzLcnF4+GuVs2s5xeoP9dvVp1GbizSTfuDaz55vYasyvjTM0tanP1Plv0qzNgpxbSZ4Peq3yNOsz0azTdQ4b8+fD3Yv5zsvLTJ+KPEkOdjZuvU/nYHPhulfj6zThzQfvhyeYAQAAAABoxQAzAAAAAACtGGAGAAAAAKAVA8wAAAAAALRSaq0PnaG1brdbvawbAAAAAGC1Sinvaq3d2XZPMAMAAAAA0MqThw4AAAAAMHG618/Zzmj+cM7682yMZoab0yuOrj/us5NkLcd5+mL7g8+TJNlKztZHmS4yzDPcvMiye3J84/Fb5xlnSka5kuRNNpLBZjonF6uylpszJElvsJ8kWfab67P1ucgxz7g2k2txm0xL53nbz+v1y+Xz4e7lwlHSOVnqcFNeDXoffp7G/XUXU9d4uJnB53pJlu8/tOMJZgAAAAAAWjHADAAAAABAKwaYAQAAAABopdRaHzpDa91ut3qXCgAAAADAapVS3tVau7PtnmAGAAAAAKAVA8wAAAAAALTy5KEDAAAAAEycvu0nSc7Wk8Mk54Pd7J4ctzpWb7B/pW3ZV23eV555WVrl2etfaXu9c/N+58PdJMnuy8vs91Kf2Txbl7MXNctGMty8XHF0OfvsZDRdy3RNJ9nuoz5nO6Mci8yrTTNDU5s8BzsbU22dg835G49tzSzP1maiN9h/73kWZblLnmRxH55cm1s7SgYHvYtFr9Z9PzzBDAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvtVu9SAQAAAABYrVLKu1prd7bdE8wAAAAAALTy5KEDAAAAAEycvu0vvc/ZenI4nj/PRjLYzLOT0fJajpMkvcF+kmTZb0K3yfN6/XL+ofKcrU8vf93B7rXbPx/02uXZm5NnazrHYS7rkCSdk6lNLkxqk7yf6zWp0U21Wctx+zzj+rzeubruPBvzdxpuTi12Xo6mszV7Puh9avJMDnbba3KTtv2ZdjzBDAAAAABAKwaYAQAAAABoxQAzAAAAAACtGGAGAAAAAKCVUmt96Aytdbvd6mXdAAAAAACrVUp5V2vtzrZ7ghkAAAAAgFaePHQAAAAAgInTvX6ylZytJ4cz686zkQw3pxuPks5JspVkLcdXjtcb7E8tL/tN6EeZp+FsJ3kz2E3n5PbH2FrQ/nzQu5c8E4dp1Oho1DYv5yTPpF7NGj2W+qzlOL3B/tJ5PjntX+k385wPd2/cpvNyNN1u1OlDz9P/vtPFx1/ims16NeglWb7/0I4nmAEAAAAAaMUAMwAAAAAArRhgBgAAAACglVJrfegMrXW73epdKgAAAAAAq1VKeVdr7c62r+wJ5lLK15ZSfqSU8qdKKT9eSvl94/ZfXUr54VLKV0op/34p5W8at//N4+WvjNd/46qyAQAAAABwd6t8RcYwyW+ptf76JBtJ/v5Syrcl+VeT/MFa63qS/yLJ5PdFd5L8F+P2PzjeDgAAAACAR+rJqg5cR+/e+Gvjxa8Z/1eT/JYkz8btfyjJXpKDJN8+nk+S/yDJ/76UUuqH/A4PAAAAYCmne/2c7SSHjbbzbNz+AMPN5Gg0++wkeT7oTa1e9lWb95ln9+Q4vcH+nfJ8fNq/3fnH5+2cJFszq9ZynCRXsrTJM6lPclmjG/M1apLMz5iMrt3K6pMkg810Tkazi84/a+n6vO3n4Kd3p9o6J8n2+BrcRW+w/8HnWep6TQw3R9Oj0T3eNNu3vVr3/Vjpj/yVUn5pKeVHk/yFJF9McpLkr9Raf368yc8k6YznO0lOk2S8/ueS/G2rzAcAAAAAQHsrHWCutf5CrXUjyTck+dYk/+27HrOU8h2llC+VUr701a9+9a6HAwAAAACgpZUOME/UWv9KkrdJNpP88lLK5NUc35DkbDx/luRpkozXryX5S3OO9b211m6ttfvRRx+tOjoAAAAAAAuUVb3iuJTyUZL/utb6V0opf0uSQUY/3PdPJvn+WusfLaX8H5P8WK313yqlfD7Jr6u1/s9KKZ9L8g/WWv/h687R7Xard6kAAAAAAKxWKeVdrbU7276yH/lL8vVJ/lAp5Zdm9KT0F2qtP1BK+Ykkf7SU8r9Ncpzk+8bbf1+Sf7eU8pUkfznJ51aYDQAAAACAO1rZAHOt9ceSfMuc9p/O6H3Ms+1/I8k/tKo8AAAAwON3utefWj7bSQ7nbHc+3L3aeHQ5++wkeT7oXdlk2W9CP7Y8H5/2L893NL3u2clSh7rQzNW6PlvT7a/Xr257UaOZusxay3F6g/1WeR5bfT457edNNuavHG5ebTu62jSrc5Jsj2v0oeeZvb9e70yvn/SZzsvLtmZXa/aVebz54P14L+9gBgAAAADg08cAMwAAAAAArRhgBgAAAACgFQPMAAAAAAC0UmqtD52htW63W72sGwAAAABgtUop72qt3dl2TzADAAAAANDKk4cOAAAAADBxutfP2c7l8uF4ep6N0cxw8+aDHF3Odk4u57dznKcvtj/oPJ+c9vNmcu7G+Tsvb953azxdy/FFW2+wP7XNst8UP93rXxz4bH00PRyvm1ujRi2S6XpsNbJNci2b5+PT/uV5m+c+unq+eWZrdNf6fHzav/W2U7nHdg++9mJ+NssvujzD3cuFmeu53ejTzXzefPB+eIIZAAAAAIBWDDADAAAAANCKAWYAAAAAAFoptdaHztBat9ut3qUCAAAAALBapZR3tdbubLsnmAEAAAAAaMUAMwAAAAAArTx56AAAAAAAE6d7/ZztjOYPx23nw90kSeflaHlr3P580Fv6+Mu+anNhnqOkc/IAed72kyRn69Ptb7Ix3TDYXHiMZyeLj//ZF0+Xy9OoT3JZo4nz2VzDzeTo5lyTWrapT7M2h/MyXFOb2RxNzwe9pfN8ctq/cZvDBe2fOdi9mH+1oG996HlO9+bk2bratMjr9cvPh2cvr96DXq37fniCGQAAAACAVgwwAwAAAADQigFmAAAAAABaKbXWh87QWrfbrd6lAgAAAACwWqWUd7XW7my7J5gBAAAAAGjlyUMHAAAAAJg43esnW8nr9eQ8G6PG4eaV7Tovbz7Wdo6TJL3B/kXbst+Efmx5Pjnt57CxfJHpOoNR3s7JKEPz/LNa1SdJtkaTs/XRdGHG4WZyNH2M63K97/q8GvSu3bRNnllvZjPN6U8LHSWDg8uMD52n83K6ZnfJc7hgmxuv4by8R6Pr+fkXT5fKQzueYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVgwwAwAAAADQSqm1PnSG1rrdbl325eEAAAAAACynlPKu1tqdbfcEMwAAAAAArTx56AAAAAAAE6dv+1PLZ+vJYZLzbExvONwcTY+uHqNzMppu53iqvTfYz7LfhH50efZGec52xjmGuwvPO+vZyfXrnw96rfMkSbZGkys1Gm5eydc5ubLblLUc37k+b+6xNkn7+rzeGc3P9pnOweZSx2ra+hTlmedsZ3r5MI2+nly5prsno/4yy5sP3g9PMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u9W7VAAAAAAAVquU8q7W2p1t9wQzAAAAAACtGGAGAAAAAKCVJw8dAAAAAGDidK+fs53R/GGS82wkw81Rw9Hldp2TZKux31qObzx2b7CfZV+1+ZjzNL3JxuXCYDOdk9sfc3uctW2eKVvJ2frl4mHGNWtq1G9ezkkdnw967fLMZJibY7iZzsvbH3c7x+3q87Y/t302XzLOONyd6leLdE6SV23q89jyzPafZqZxP38z238m5zzYvJh/NejN3cardd8PTzADAAAAANCKAWYAAAAAAFoxwAwAAAAAQCul1vrQGVrrdrvVu1QAAAAAAFarlPKu1tqdbfcEMwAAAAAArTx56AAAAAAAE6d7/dHM1mXb2fr0Nm+ykQw3RwtHV4+xe3Kc3mB/7vGX/Sb0Y8xztnO1/TDJeTaSweZSx5sYHPRa55lr67JOhzOrzoe7lwtHSefkYpckyVou63Wf9bk495xrdJM71Wdr/rrZfjTPm2atZjN9rvfpyLPA2U7yZrD4fFfOP75GTd588H54ghkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u9XLugEAAAAAVquU8q7W2p1t9wQzAAAAAACtPHnoAAAAAAATp3v9ZCs5W7+67jDJ+XA3nZej5VeD3tLHX/ab0I8yzy2c7SRvsjFaGGxeu+2zk2Qtx0mSpy+27yfPuGZvsrHw/J2TZGumbZJj4t7yzDjY2RjN3KI2zVx3yjP7j53j9Uw/+x0Huwu3fT7o3a3/PII8/dMvLNU/l9Eb7C+dh3Y8wQwAAAAAQCsGmAEAAAAAaMUAMwAAAAAArZRa60NnaK3b7VbvUgEAAAAAWK1Syrtaa3e23RPMAAAAAAC0YoAZAAAAAIBWnjx0AAAAAICJ073+/BVbydl6cpjkPBtX1w82L2Y7J1O7XVjLcZ6+2H4UedZynCTL53nbz9n65fJhY935cDedl4v33Vq8Ks8HvSTJsq8iPd3r31yLieFmcjTdNKnNbLb7qM/huO0i03CzdX2SUY3a1mcZB+sb128w2Myzkzv050eU55PTq/fXm3l9aLh5pWnRtWz+8z774ulSeWjHE8wAAAAAALRigBkAAAAAgFYMMAMAAAAA0EqptT50hta63W5d9t03AAAAAAAsp5TyrtbanW1f2RPMpZSnpZS3pZSfKKX8eCnlO8ftv76U8idLKX+6lPJ/K6X8ssY+v7eU8pVSyk+VUn7rqrIBAAAAAHB3T1Z47J9P8ntqrV8upXwmybtSyheT/DtJ/he11qNSyj+V5F9M8r8upfyaJJ9L8muT/Mokf6KU8s211l9YYUYAAADgETnd61/Mn+1cth8mOc9GMthM5yTZusWxng96V9qW/Sb0Y84zyfRmsDvV1jlZvP92jtMb7C9cf9c8s4U4Wx/XajjOeHSZr7npWo6vHLs32L+XPGfrt6/RVuZfp4ll83x82h/1k+sMN291rM7Ly/lX44yfijzja3Ndv72N2Xvw+aC3dB7aWdkTzLXWn621fnk8/1eT/GSSTpJvTvKfjDf7YpL/yXj+25P80VrrsNb655J8Jcm3riofAAAAAAB3815+5K+U8o1JviXJDyf58YwGk5PkH0rydDzfSXLa2O1nxm2zx/qOUsqXSilf+upXv7qyzAAAAAAAXG/lA8yllK9L8v1JvqvWep7kn0ryz5ZS3iX5TJL/apnj1Vq/t9barbV2P/roo/sPDAAAAADArazyHcwppXxNRoPLr2utfyxJaq1/NsnfN17/zUn+gfHmZ7l8mjlJvmHcBgAAAADAI1Rqras5cCklyR9K8pdrrd/VaP/ba61/oZTyS5L8n5Mc1lpfllJ+bZJ/L6P3Lv/KJP9xkm+67kf+ut1u9bJuAAAAAIDVKqW8q7V2Z9tX+QTzb07yO5P86VLKj47b+km+qZTy+fHyH0vyKklqrT9eSvlCkp9I8vNJPn/d4DIAAAAAAA9rZQPMtdYfSlIWrP43F+zz3Um+e1WZAAAAgMftdK9/ubCVnK0nb7Ixd9vOwebc9q3G/FqO0xvsXywv+03oR51nka3k9XpyPtydbj9KOifTeWYztcozrss8h43580ndhnPqdDSaNPM9H/TuvT5nO6NMV2rTyPHsZLrpzvVpnHvWYWP+fLibzsvbH/vVHevzGPJ8cnqL/jx2mOR8sJvOzPXZyugazfP0xfZSeWhn5T/yBwAAAADAp5MBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u3XZd7sAAAAAALCcUsq7Wmt3tt0TzAAAAAAAtPLkoQMAAAAATJzu9XO2kxzOWXeejfk7DTcv54+mV3VOku0cXyw/fbH9Yed527+YP1u/uv7wulzNfEejLK8GvalVy35T/HSvn2xN5zkcr5vKMbisSedk+hhbSdbGNekN9u+WZ1yfebVJkjdL1GYryfP7qE/Dwc4152/2mySDz/UWbPjpzTPP2c708uw17BxsXunHbfPQjieYAQAAAABoxQAzAAAAAACtGGAGAAAAAKAVA8wAAAAAALRSaq0PnaG1brdbvawbAAAAAGC1Sinvaq3d2XZPMAMAAAAA0MqThw4AAAAAMHG610+2LpcPnu5ezHdeLnesrSRrOU5vsH/Rtuw3oR9lnhuc7Yymh4228+HuvE3TeXn5z3s+6N25PlM51kcZZs89qdtkt7UcT61fVX3Odm5Xk0UZe4P9VnmunDcbo5nh5rV9aNJf5pnU6EPP8/Hpzf15kUX9KkleDXqt8tCOJ5gBAAAAAGjFADMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtFJqrQ+dobVut1u9rBsAAAAAYLVKKe9qrd3Zdk8wAwAAAADQypOHDgAAAAAwcbrXn1o+27mcP0xyno3LhuFmcjSa7ZwkW4391nKc3mD/yvGX/Sb0VJ6t5Gz9YfN8cjpdn6kMjfM3dU6ml7eubnLhsy+eLpXnoj7jg56tz880m2FRXVrF/AABAABJREFUnrUcJ8lFrZa+Xm/7U9foTfP6DDantl2U6d7rM3PAqRpd02fmmdQnGdWoVX9+RHlm+/N13gx2F657NudaruU4T19sL5WHdjzBDAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvt1mXf7QIAAAAAwHJKKe9qrd3Zdk8wAwAAAADQypOHDgAAAAAwcfq2nyQ5Wx8tHyY5H+5ebnCUPDtJ1nI8tV9vsH+r4y/7TehmnsNxWzNP52WylfeYZ69/MX+2c5mpmavz8vbHm2Sf5G2VZ+vyeiXTmWazJUmOks7J9TkmWtdnK3ndyHSejWS4meT91+ds53bbHmamTtd49jJ5Puh9KvIkGRX6lprXNRllnFzT7Zk+5M0H74cnmAEAAAAAaMUAMwAAAAAArRhgBgAAAACgFQPMAAAAAAC0UmqtD52htW63W72sGwAAAABgtUop72qt3dl2TzADAAAAANDKk4cOAAAAADBxutefv2IrOfjp3XROLhaTJGs5vvZ4vcH+1PKy34R+lHm2ktfro+Xz4e7VjY4uZyf5mrYbGe+c5+1lfc7Wk8Mk59m43GC4OZVnUa5Fme5Sn/uuTZs8H58u6D9ZkK9pTt2SZHDQ+1Tlufa8M+dsXrPtW9xr3nzwfniCGQAAAACAVgwwAwAAAADQigFmAAAAAABaMcAMAAAAAEArpdb60Bla63a71cu6AQAAAABWq5TyrtbanW33BDMAAAAAAK08eegAAAAAABOne/3ldthKXq8nnznYzVajeS3HSZLeYH9q82W/Cf1B5NlKztavNh/OLJ8PdvPs5Prjf/bF07vnmcl0mOQ8G8lwM0nSeTm12ZTZOq2yPpNsE7/jYPfaY6/lOE9fbC+fZys5WN9Yar8k6RxsZntcj3l6g/129XmEeW5zfW5j0sefD3pJlu8/tOMJZgAAAAAAWjHADAAAAABAKwaYAQAAAABopdRaHzpDa91ut3qXCgAAAADAapVS3tVau7PtnmAGAAAAAKCVJw8dAAAAAGDidK8/3bA1vXi2Pr38ZribHCWdk+ld1nKcJOkN9qe2X/ab0I8yzzjD6/Wr688Hu1PnXhD7Is9sprvkmThbTw6bmYa7o5lGXa6rSdN91+czB7tX2m5bm7vmuclsX5o4HE/Ps5HOweZF+6tB74PP88lpf6qv3OR8MH39ns309ea1S5KnL7aXykM7nmAGAAAAAKAVA8wAAAAAALRigBkAAAAAgFYMMAMAAAAA0EqptT50hta63W5d9uXhAAAAAAAsp5TyrtbanW33BDMAAAAAAK08eegAAAAAABOnb/tTy2fri7c9THKejdHCcDM5SjonyVaStRwnSXqD/al9lv0m9GPMsyjDm8Funp3c/liTTBO9wf7yefam65Ot0WQ242HGtRlujhrGtZnsspbjK7VJ2l+vhTUa7ubZy9sfb/a63bk+E1vzmw9+evfGYz47uazXQ+eZ9O/ng16SdvV5vZOcD+ef56ZrNduHk+l7zJsP3g9PMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAKwaYAQAAAABopdRaHzpDa91ut3pZNwAAAADAapVS3tVau7PtK3uCuZTytJTytpTyE6WUHy+lfOe4faOU8kkp5UdLKV8qpXzruL2UUr6nlPKVUsqPlVJ+w6qyAQAAAABwd09WeOyfT/J7aq1fLqV8Jsm7UsoXk/z+JL+v1vofllJ++3j5703y25J80/i/35TkYDwFAAAAfpE43etfbdwaTV6vXzadD3cvF46Szsn0Lts5vnKY3mA/y34T+rHl+eT0Ms/h5NzZmN5ouHmrY3VeTi+/GvTa1Wdruu1sfZRtKtck09H43OP6zOyaJFnLcXqD/ST58Ovztp+Dn97Ns5Obt73O80FvbnubPG++uDu37g+R5+PT6ftr6j5qafC5y2zefPB+rOwJ5lrrz9Zavzye/6tJfjJJJ0lN8svGm60l+c/H89+e5A/XkU+S/PJSytevKh8AAAAAAHezyieYL5RSvjHJtyT54STfleSPl1L+9YwGuP/u8WadJKeN3X5m3PazM8f6jiTfkSS/6lf9qlXGBgAAAADgGit7gnmilPJ1Sb4/yXfVWs+T7Cb552utT5P880m+b5nj1Vq/t9barbV2P/roo/sPDAAAAADArZRa6+oOXsrXJPmBJH+81vpvjNt+Lskvr7XWUkpJ8nO11l9WSvm3kxzWWv/IeLufSvL31lp/dtHxu91u9S4VAAAAAIDVKqW8q7V2Z9tX9gTzePD4+5L85GRweew/z+U73H9Lkv90PP8myT9RRr4to4HnhYPLAAAAAAA8rFW+g/k3J/mdSf50KeVHx239JP/TJP9mKeVJkr+R8fuUk/xgkt+e5CtJ/nqS37XCbAAAAMAjdLrXn24YP6J2tp4cjpvOs5EMN9N5ufg42zlOb7B/pX3Zb0I/ujxv+zlY3xgtDDfnbvPsmhyLrI3z3ak+W9PrztZH0zfZGM2M807qNLP5lTxJ8vTF9nJ53vZztj4+5yOoT/8rpzdvNHF0u812Ty770sry3DLLXfNc9J+tXPbriTnX76Zr93zQm1r25oP3Y2UDzLXWH0pSFqz+787Zvib5/KryAAAAAABwv1b+I38AAAAAAHw6GWAGAAAAAKAVA8wAAAAAALRSRq8+/jB1u93qZd0AAAAAAKtVSnlXa+3OtnuCGQAAAACAVp48dAAAAACAidO9/uXC1mhytp4cJjnPRjLcTI5G7c9Oljv2Wo7z9MX2o8izluMkaZdna5ThJoczy+fZuFwYbKYzzjv+Z+X5oJdlvyk+W59JrsNM12f2XLMm9egN9qfa2+Q527l5u8OZ5YvaNPLOZm1Vn7f9ue2vG9dv6rrMM9yc2zz43Kcgz978PLNmr+nhePqZg91sZXRt5vHmg/fDE8wAAAAAALRigBkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWim11ofO0Fq3261e1g0AAAAAsFqllHe11u5suyeYAQAAAABo5clDBwAAAACYON3rj2a2RpOz9ct1h+PpeTZGM8PN6Z2Pks7J1O5Zy3F6g/2LTZb9JvSjzTP2emd0/s7B5oI95tuak6V1nq0b6pIkw810Xl6fZ6KZ6y71aVubZp7ng95U+7J5Pj7tL1x3Pti96B/LejXO9anO0+w/yZX767r+NMnkzQfvhyeYAQAAAABoxQAzAAAAAACtGGAGAAAAAKCVUmt96Aytdbvd6l0qAAAAAACrVUp5V2vtzrZ7ghkAAAAAgFaePHQAAAAAgInTvf7F/Oudy/bz4W5yNL3ts5NkLcfpDfZvffxlvwn9KPNsTbe9Xp9e/h0Hu9ce47qMd6nPhUa+s/XkMMl5NkYNw83kKOmcTG+6luOpQ0zy3Ud9kukaPUR9zsZ9582kDoPNhftMatM055+U54PepyPP1tU+POuma9bUvH7efPB+eIIZAAAAAIBWDDADAAAAANCKAWYAAAAAAFoxwAwAAAAAQCul1vrQGVrrdrvVy7oBAAAAAFarlPKu1tqdbfcEMwAAAAAArTx56AAAAAAAE6d7/anls53L+cMF+5xnIxlujhaOLts7J8lWkueD3kXbst+Efmx5PjntX3vuiwzD3alzNzMs8mrQu1N9JrU5zGUNOi9vd5zZukzcd30urs1Mba6ry0Sb+kzyzPMmGzcfYLC5eNXBh5/ndK8/dU8t63BmedLvBwejvuTNB++HJ5gBAAAAAGjFADMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtFJqrQ+dobVut1u9rBsAAAAAYLVKKe9qrd3Zdk8wAwAAAADQypOHDgAAAAAwcfq2fzH/ej05H+4mSTov2x/z1aB3Mb/sN6Enec7WkzfZSIabD5rn49NRnkldmnZfHrfK0xvst85zutdPtkbzZ+uj6eHMNufZGM0MN5Oj0WznZDTdznTmZpY2eSb1Sa7W6CHq88lp/0o95rmo0TyNujUNDnp3qk/rPIPN+c0t8pzu3S7P653p5X/s4NuSXO0vs7z54P3wBDMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtFJqrQ+dobVut1u9SwUAAAAAYLVKKe9qrd3Zdk8wAwAAAADQypOHDgAAAAAwcbrXH81sJWfryWGS82yM2oabyVHSOUm2rjnG80Fv4bplvwn9GPO83mlkWGSwmd2T46WOnSRPX2wvnWfyjz9bn153mJmcw83R9OjqcZ6dJGu5zNsb7CdpV5+zneTNI6lP/yun128wpxYTnZP57a8a/WnZ+jy2PBf318RWcrC+MX/j4WZ2X86/ZpP+MsubD94PTzADAAAAANCKAWYAAAAAAFoxwAwAAAAAQCsGmAEAAAAAaKXUWh86Q2vdbrd6WTcAAAAAwGqVUt7VWruz7Z5gBgAAAACglScPHQAAAABg4nSvf7mwdXX96/XL+fNsJIPNdE4uN13LcXqD/YXHX/ab0I8uz9t+zsbnPGy0n2fj6sbDzeTo9sceHPTa1Wf8jz1bn143yTeVbbg5ms7J9exkVK8kFzVbWX2uyTHP7snoOi6bp3/6hdtvPNi8cZNnJ5fzzwfLX6/HlueT0/6N27wZ31dtvNh52mo/luMJZgAAAAAAWjHADAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvt1mVfHg4AAAAAwHJKKe9qrd3Zdk8wAwAAAADQypOHDgAAAAAwcfq2nyQ5W0/eZCMZbN5qv87JaLqd4yvreoP9i/llvwn96PLs9Rev3JrffLaeHI7nz4e7ydF0xuZun33x9G55ZjK8Xk/OszFaGGxenHOeya5rOb6o0b3WZ06+iUmNzhvXeLY+aznO0xfby+V5e0Oea5ytX217M9xNkgw+10vSvj8/mjyN63W2s/j4F44y1YfmXc7ng97FvDcfvB+eYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVkqt9aEztNbtdqt3qQAAAAAArFYp5V2ttTvbvrInmEspT0spb0spP1FK+fFSyneO2//9UsqPjv/786WUH23s83tLKV8ppfxUKeW3riobAAAAAAB392SFx/75JL+n1vrlUspnkrwrpXyx1vqPTDYopfyBJD83nv81ST6X5Ncm+ZVJ/kQp5Ztrrb+wwowAAADAI3K618/ZzvXbvMlGnh1s5vmgt/Txl/0m9GPMc6Ot5Gz9avObbFwuDDbTOZle/2rQa5dna7rtbD05THKejXQONmejJUnWcnzlWL3B/pW2e6/P1mXGpptqs53j9Ab7d67P68Z5z4e76bxcGPFGz+/hej14nreX12vSb+Y5b16fiTnXaaLt9aKdlT3BXGv92Vrrl8fzfzXJTybpTNaXUkqSfzjJHxk3fXuSP1prHdZa/1ySryT51lXlAwAAAADgbt7Lj/yVUr4xybck+eFG89+T5P9Ta/1Px8udJKeN9T+TxoA0AAAAAACPy8oHmEspX5fk+5N8V631vLHqH83l08vLHO87SilfKqV86atf/ep9xQQAAAAAYEkrHWAupXxNRoPLr2utf6zR/iTJP5jk329sfpbkaWP5G8ZtU2qt31tr7dZaux999NFqggMAAAAAcKNSa13NgUfvWP5DSf5yrfW7Ztb9/Ul+b611q9H2a5P8exm9d/lXJvmPk3zTdT/y1+12q5d1AwAAAACsVinlXa21O9v+ZIXn/M1JfmeSP11K+dFxW7/W+oNJPpeZ12PUWn+8lPKFJD+R5OeTfP66wWUAAAAAAB7WygaYa60/lKQsWPd8Qft3J/nuVWUCAAAAHrfTvf7F/NlOcpjkfLibZy9v3nctx0mS3mB/4TbLfhN6bp7Bbp6dPFCet/2crSdvhrvXbte5Rb2SZHucceLpi+3l8jTqkyTZmr/d2XryJhvJYDOdk+lN12YyTPQG+63qkySv10f9Zp62tUla1OdtP6/Xb7fteTZu3mi4eXEvPB/0WtXnMeX55LSfw5vOP9y81bHmXdfPv3h6tZF7t/If+QMAAAAA4NPJADMAAAAAAK0YYAYAAAAAoBUDzAAAAAAAtFJqrQ+dobVut1uXfXk4AAAAAADLKaW8q7V2Z9s9wQwAAAAAQCtPHjoAAAAAwMQnp/0cJjnPxu12GG4mR0nnZPEmW0meD3pJkmW/Cf3Y8nx82p9avsg13FzqODkaTQYHvanmZfOc7vVztjOaP1wm09HlbOdkVJMkWctxeoP91nma9Zm6Zg9Yn4mzncsaXed8sDu1vHtyfGWbSY3ukuf1zu32WWWe2f48dd7h7sJ1Sab60KzJdfPmg/fDE8wAAAAAALRigBkAAAAAgFYMMAMAAAAA0EqptT50hta63W71LhUAAAAAgNUqpbyrtXZn2z3BDAAAAABAK08eOgAAAADAxOleP2c7yZvhbp69nF73fNC78/GX/Sb0Y81zeM0259lIhpvJ0WXbs5Prj7uW4/QG+63yJEm2rq57vT7K0jnYvLLJWo4XHrM32L+Yb5Vna3TuRc6zkQw2L5Zvqk1yx/rM1OZsQbbDSb7h7q2OPfhc74PP8/Fp/9bbnmfjdjl6v/9i3psP3g9PMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAKwaYAQAAAABopdRaHzpDa91ut3pZNwAAAADAapVS3tVau7PtnmAGAAAAAKCVJw8dAAAAAGDik9P+3PY3w93k6HK5c5K8GvSWPv6y34S+bZ5nJ8nz95Dn9O10nrP1Rp6GZy+TtRzfeLzeYP9e87xeTz5zMMqyfc35Z8+7yF3zJKMaTeqz+/LmmkzMy7h0nr3LPGc7yZtsJEk6B5tLHWfWpO9/8Hne9nPwdPfmDRtu27eT5OmL7aWOTTueYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVkqt9aEztNbtduuy73YBAAAAAGA5pZR3tdbubLsnmAEAAAAAaMUAMwAAAAAArTx56AAAAAAAE6d7/auNW4u3f72enA93kySdl8l2jpMkvcH+3O2XfdXmlTzXZHmMeW7jbD3pHHxbeoP9u+W5IcvZ+mh6mHGNjpLOyWi354Pe3H0+9Pr0v+90qe07J9evfzVTpw89z9z7vWnB9Zv0pWR0bZL595hX674fnmAGAAAAAKAVA8wAAAAAALRigBkAAAAAgFZKrfWhM7TW7Xard6kAAAAAAKxWKeVdrbU72+4JZgAAAAAAWnny0AEAAAAAJk73+qOZrdHkYH3j5p2Gm8nR5WLnJNnO8cVyb7B/Mb/sN6GXzjPcHE1Xledtf2r5bD05THI+3F24z+7L44XrZj19sb1cnr3+RW1er4+m59m43GB8bTonl03jzbOW63P1Bvt3rs9FpgX1efby5hxNy9anf/qFxSvn9JVlDA56S9dnVXk6J8mrwfJ5Tvf6eb1ztf26/jyr2b+b91ay/P1FO55gBgAAAACgFQPMAAAAAAC0YoAZAAAAAIBWDDADAAAAANBKqbU+dIbWut1u9bJuAAAAAIDVKqW8q7V2Z9s9wQwAAAAAQCtPHjoAAAAAwMTHp/1r159nIxluXmnvvJy//atBb2p52W9CP7Y8p3vX50mSbI0mZ+uXTYczm5wPdtM5uYc8b6/mmZy3ec4rdToaTTonyXaOkyS9wf6VY917fW5Tm+FuOi9HuWYzLZunf/qFZHC1fySjf/tNUZ/PXJ9ZH3qeef3nts7WR9fufLA7d/3uyXGevthufXxuzxPMAAAAAAC0YoAZAAAAAIBWDDADAAAAANBKqbU+dIbWut1uXfbdLgAAAAAALKeU8q7W2p1t9wQzAAAAAACtGGAGAAAAAKCVJw8dAAAAAGDi9G3/StvZ+mh62Gg7z8b0RsPN0fQo6ZyMZrdznN5gf2qzZV+1uSjPtVnm5NlKsnYfefam85ztzN9uKt9w93LhaHq7Sa1eDXr3kidb04uTa9fMdFGv4ebcPM3rduc8mV+jw5nlixodzW45ynTX+vzcf7V77XZrOV7quEnSG+x/avIki/vyrMMk54PF+QcHvYt5r9Z9PzzBDAAAAABAKwaYAQAAAABoxQAzAAAAAACtlFrrQ2dordvtVu9SAQAAAABYrVLKu1prd7bdE8wAAAAAALTy5KEDAAAAAEyc7vUvF7ZGk7P15HDcdJ6N0cxw82KzzsvpY2zNHHMtx0mS3mA/y34Teuk8R0nn5D3kmTno2fr08mGS33Gwe+vjruX4znkmGQ4zrsuCazRbj1XlmbhrbSaeD3rt8oxr82aw3Dln+1HTq0EvST4deWac7Sx1iFF/G47+Lc9ejq7ThDcfvB+eYAYAAAAAoBUDzAAAAAAAtGKAGQAAAACAVgwwAwAAAADQSqm1PnSG1rrdbvWybgAAAACA1SqlvKu1dmfbV/YEcynlaSnlbSnlJ0opP15K+c7Guv95KeXPjtt/f6P995ZSvlJK+alSym9dVTYAAAAAAO7uyQqP/fNJfk+t9cullM8keVdK+WKSvyPJtyf59bXWYSnlb0+SUsqvSfK5JL82ya9M8idKKd9ca/2FFWYEAAAAHpHTvX6ylZytJ4dJzrMxf8PB5sJjdE6utm3nOL3Bfpb9JvSjy/O2P7f99fo12YabyVHybE6OJFkbZ0nSOs/Z+mj5sLHu2jxJcjSadE6SrZkcE/dRn9frN+SZZFpQo+eDXus8n5zOv14Tb5qZhov70IVGzV4Nevea5zAzNXoPeU73LvOc7Vy/7UWtZnONMwwOepnlzQfvx8qeYK61/myt9cvj+b+a5CeTdJLsJvm41jocr/sL412+PckfrbUOa61/LslXknzrqvIBAAAAAHA37+VH/kop35jkW5L8cJJvTvL3lFJ+uJRyVEr5jePNOklOG7v9zLht9ljfUUr5UinlS1/96ldXnBwAAAAAgEVWPsBcSvm6JN+f5LtqrecZvZbjb03ybUn+xSRfKKWU2x6v1vq9tdZurbX70UcfrSQzAAAAAAA3K7XW1R28lK9J8gNJ/nit9d8Yt/1HSf7VWuvb8fJJRoPN/3SS1Fr/lXH7H0+yV2v9k4uO3+12q3epAAAAAACsVinlXa21O9u+sieYx08lf1+Sn5wMLo/9X5P8D8bbfHOSvynJX0zyJsnnSil/cynlVyf5piQ/sqp8AAAAAADczZMVHvs3J/mdSf50KeVHx239JC+TvCyl/Jkk/1WSf7KOHqP+8VLKF5L8RJKfT/L5WusvrDAfAAAAAAB3sLIB5lrrDyVZ9G7lf3zBPt+d5LtXlQkAAAB43D457edwpu08G6OZ4eZoepR0TkazW0meD3q3Pv6yr9p8bHlO9/qjkyR5vd7I0tTIdZNnJ9N5l87ztp8kOVu/bDucs915Ni5zTYzz7Z4cpzfYn3v8u+Q5zN3qM1ubVnn2Rnma12yeufVpZJz0r4ntjGr2achzsLNx84aDOVkWbXrQvj/Tzsp/5A8AAAAAgE8nA8wAAAAAALRigBkAAAAAgFbK6Pf1Pkzdbrd6lwoAAAAAwGqVUt7VWruz7Z5gBgAAAACglScPHQAAAABg4nSvn2yN5l+vj6bnw910Xl7ddjvHSZLeYP/Wx1/2m9Czec6zkQw3Hy7P2/5UXW7taDTpnMxfvZ3j9Ab77eqTJFvJ2XpyOG4/z8ZoZrh5Jcckw9ac462Na5ikXZ4V1Kd5Xe9Sn4Of3s2zBfVf1vNBL0nL/vyI8nxyOsrzJhvJYPPabRf13UVeDXpL56EdTzADAAAAANCKAWYAAAAAAFoxwAwAAAAAQCu3fgdzKeXvTvKNzX1qrX94BZkAAAAAAPgAlFrrzRuV8u8m+buS/GiSXxg311rrP7e6aDfrdrvVy7oBAAAAAFarlPKu1tqdbb/tE8zdJL+m3mY0GgAAAACAXxRuO8D8Z5L8nUl+doVZAAAAgF/kTvf6ydZo/mx9ND1Mcp6NZLh5/c5HSefkcnE7x0mS3mD/om3Zb0I/yjxJznYu2w6TnA92b9z32cn1658Peq3zLDLJeZjkfLibHI2WJ3XZmrPPWo4vanSXPM0avbmhPjfVJmlXn49Pr69PMu5LEzf1qSSdl6Ppq09Bnk+uyfOm0V9uY/fkeGq5N9hfOg/t3HaA+Vck+YlSyo8kGU4aa63bK0kFAAAAAMCjd9sB5r1VhgAAAAAA4MNzqwHmWutRKeXvSPIbx00/Umv9C6uLBQAAAADAY1du87t9pZR/OMm/ltErc0qSvyfJv1hr/Q9Wmu4G3W63epcKAAAAAMBqlVLe1Vq7s+23fUXG/yrJb5w8tVxK+SjJn0jyoAPMAAAAAAA8nF9y2+1mXonxl5bYFwAAAACAT6HbPsH8H5VS/niSPzJe/keS/OBqIgEAAAC/WJ3u9UczW5dtr9cv58+zkQw38+xlspbj9Ab7Sx1/2VdtPrY8H5/2R+ecGG5Ore+8vN1xtmaWnw96rfKc7vUvDnalLhOD6YxNnZPpTGs5TpKLOi6d520/B+uNcw+vnrtNjdrW55PT/sJ1h0sc53ywm+SyXtvjvvah55ncX693cm2/XqR5LV+Nr1GTV+u+HzcOMJdSSpLvyegH/v574+bvrbX+X1YZDAAAAACAx+3GAeZaay2l/GCt9dcl+WPvIRMAAAAAAB+A275H+cullN+40iQAAAAAAHxQSq315o1K+bNJ1pP8Z0n+yyQlo4eb/zurjXe9brdbvUsFAAAAAGC1Sinvaq3d2fbb/sjfb73nPAAAAAAAfOBuO8B882POAABAa5NfUZ/6yfqx1+vJZw52r6xay/G1x+wN9i/m2/6q+6PLM7GVnK2PfvH+PBuXvzZ/NPpF+zmxr2S87/pM8iTJ+XD3on3yC/fNTPNqdW/1GZ/o9fp0beblaFpUmzvlGV+npsPx9Dwbiw8w3EyORrOdk4tDZS3H6Q327z3PtVlWnCeZ7jsTF9fuaLr92cn8Yz4f9C7m7/P+usgy2Lz23pqY9KM79+dGbZLkTaMek2uwPedct/HB53k7fb1eN/p087NnytH85onBwR36TyPPbF++cm9NPqtncj07me7DTQ9dn85J8uou99fb/pXPnXneNP+W3aDz8jLTyvIsqtUK8jQt+lt6a41r2TlJPv/i6fLHYGm3HWD+v2c0yFySfG2SX53kp5L82hXlAgAAAADgkbvVAHOt9dc1l0spvyHJP7uSRAAAAAAAfBB+SZudaq1fTvKb7jkLAAAAAAAfkFs9wVxK+Rcai78kyW9I8p+vJBEAAAAAAB+EUuvNv99XSvmXG4s/n+TPJ/n+WuvfWFGuW+l2u3XZl4cDAAAAALCcUsq7Wmt3tv2272D+feOD/DdqrX/9vsMBAAAAAPDhue0rMjaTfF+Sr0vyq0opvz7JP1Nr9UN/AABwD073+pcLW6PJ2fr0Nm+ycXXH4WZylHROpnZNkqzlOEnSG+xn2W/+3UeerZlVd8rztp+z9eRwzrrzSY7h5vydjy5nJ3WaeDXoJcnd6tNwtjOaHs7LdjR9/vdRnxtrk1zJNbGd4/QG+0na12dSj6ZJxvNF/aeRa6LZn54Pep+aPEmudoRM32uHjfbzbFxmGl+35nWaaNt/5p1z7rmbxnVpfgY9H99XrfNcU59JP5/kefZy+t65jQ8+z9urnz9n6+PP5MHVa/Ts5Oo1udc8e/25/fj1eqPfND5ntsf1abquVqusz+7JcteqVZ4Ffy8WOdjZyKD3+3/x5Blfr9nPoLmfyU2DzYvrN0/bv1+0c9sf+fvfJfmtSf5SktRa/1SS//6KMgEAAAAA8AG47QBzaq2nM02/cM9ZAAAAAAD4gNzqFRlJTkspf3eSWkr5miTfmeQnVxcLAAAAAIDHrtRab96olF+R5N9M8j9KUpIMknxnrfUvrTbe9brdbvUuFQAAAACA1SqlvKu1dmfbb/UEc631Lyb5x+49FQAAAAAAH6xrB5hLKf/SNatrrfV/c895AAAAAAD4QNz0BPN/Oaftv5lkJ8nflsQAMwAA3IPTvf50w1Zytp4cjhfPszGaGW6OpkejSefkYvMpazlOkvQG+0mSZV8t92jzzB44o1xNFxmHu5eNR6NsW40sd87TyNLMcNjY7jwbV2o0ManV9kxt7iNP0zL12Z5Tm7vmmfSbiz7TNBjVZrbfzPaXWSvJM9yc24/n9Zd7yTPRyNV0kXFB/3l2cvW4azlOb7B/b/fXbKa5uWayPTtZwedPI9sk05UcR5fnn2dSm09dnq3k9ZzrNGvR/TfJ93zQu2huk+ds53L58Kbzju+1TqM2k67XzHGXPElytjOdZZG5GZN0Djbn5rpr/2nWKrldxmTmMzvJ7ss73u+PJM8np/2b+8zYs4PNhesW+eyLp0vvw/KuHWCutf6ByXwp5TMZ/bjf70ryR5P8gUX7AQAAAADw6XfjO5hLKX9rkn8ho3cw/6Ekv6HW+l+sOhgAAAAAAI/bTe9g/teS/INJvjfJr6u1/rX3kgoAAAAAgEev1FoXryzl/5dkmOTnkzQ3LBn9yN8vW22863W73brsu10AAAAAAFhOKeVdrbU7237TO5h/yeoiAQAAAADwIbvxHcwAAMDqXfyq+9Zl29n6aHo4Xj7PRjKc+QX1o6Rzcrm4neMkSW+wP7VZ61+Zf4R5mjnm/tr8cDM5uv54nZPLf9pajvP0xfbyeRpZkuTNvCw35Jqt1aRObetztnPZdpgF9VmQZTbXpD7PB712eWbqc2OmmVyT2mxldI2afejTkicZZTpcsN2ieyxJno3zrI3vsYneYP9e6jPJNFWfSZZb9p1Jvrb31+v1ORkmOW64x5ueNe6zu/Tn+8gzW5+2eZr3+ptmP5nJ8ezkah+ZdR+fz2c7c/rMcDOdl1f7w7xzXufO9RnujmZmarN7cn1dkvk5W//9WmTr+tWLHPz0bnZPWt5fjynP2/6Vz+bbOMx0X0tycY2b13bZPLTjCWUAAAAAAFoxwAwAAAAAQCsGmAEAAAAAaMUAMwAAAAAArZRa60NnaK3b7dZlX64OAAAAAMBySinvaq3d2XZPMAMAAAAA0MqThw4AAAAkp3v9ZOty+Ww9eZON0cJwMzlKOieX67eSrOX42mP2BvsX88t+8+9R5mnausw1cZjkPBtT+bZyVTPnJNOd84yd7Vxtu8g1Mc6XJM9Orm7/fNC7nzwzNZrKsWSN2uZp1uNwznZTdZkYbl7OH13ONrN+9sXTT0WeRZkW9ZdF1ysZ9ZuJ++o/c++vRqZ5/Xdi0oda9Z+3/bxunPvivl5C5+VoOvvZ9GnI88np6HodZkGfva05favt589199ai/jwxOfdajqf+Tkw8WH0Gl9f42cnlPdam/0xM9aPhbjovk+0b/nZep21/XpRn92X7LK3zzHz+HOxs3G7Hwfx7cHDQm1r25oP3wxPMAAAAAAC0YoAZAAAAAIBWDDADAAAAANBKqbU+dIbWut1u9S4VAAAAAIDVKqW8q7V2Z9s9wQwAAAAAQCsrG2AupTwtpbwtpfxEKeXHSynfOW7fK6WclVJ+dPzfb2/s83tLKV8ppfxUKeW3riobAAAAAAB392SFx/75JL+n1vrlUspnkrwrpXxxvO4P1lr/9ebGpZRfk+RzSX5tkl+Z5E+UUr651voLK8wIAACPwuleP9lKztan2w/H0/NsjGaGm5crjy5nOyeX81tJ1nKc3mD/om3ZV8s9yjyLjHMeTnINNtM5GZ13nrUcTy33Bvvt84xP0jx/52Czuerac8+a1Oje6rN1NduiuizKd6f6jJ3tXM4fjqcXfWjW4LJPTfrRJPOkH723PMPNuf363vI07rHDOdvN3medl5fnfj7oLTz+vfSfRt+5kmlcl2cnV3ebvdfvLc88M5156nMgufgs2F7l589jyNM4b7M/LbzHkunP7oz61uRQn33x9G55ZjU+JycOx9OL/jRxlKnP77Uc5+mL7bvnmXNtkuTNohotqM922zxbyev15Hy4O73yaP4+8+6tpslndevPn8eU5+0t768bvF6/+pm5+3L560U7K3uCudb6s7XWL4/n/2qSn0zSuWaXb0/yR2utw1rrn0vylSTfuqp8AAAAAADczXt5B3Mp5RuTfEuSHx43/e5Syo+VUl6WUv5b47ZOktPGbj+TOQPSpZTvKKV8qZTypa9+9aurjA0AAAAAwDVWPsBcSvm6JN+f5LtqredJDpL8XUk2kvxskj+wzPFqrd9ba+3WWrsfffTRfccFAAAAAOCWSq11dQcv5WuS/ECSP15r/TfmrP/GJD9Qa/1sKeX3Jkmt9V8Zr/vjSfZqrX9y0fG73W5d9t0uAAAAAAAsp5TyrtbanW1f2RPMpZSS5PuS/GRzcLmU8vWNzXpJ/sx4/k2Sz5VS/uZSyq9O8k1JfmRV+QAAAAAAuJsnKzz2b07yO5P86VLKj47b+kn+0VLKRpKa5M8n+WeSpNb646WULyT5iSQ/n+TztdZfWGE+AAB4NOb+6v3E1mgy+dX7icPM/GL6UdI5udg8SfJ80EuSdr8y/9jyzDnv4YLtz4e7lwtHl7OTPM1fvG+dJ5n+xzWcrU9nm+TpvLzcbZJhVm+wf6f6TM4/cXhl6/F1Gm5eNoyv1Wy2O9en4WxnOtNFX5kY95mJ2b4z8XzQezR51nLc/nrNZFiYY06W2Uz3cr225t/Ts6b6zkym+8zzemf682Ry7yQLb7srmvfYJMu95BlsXtwvD5Lnbf/qZ8zsPT2j8zLZXvCZM+vpi+3l8jQ+Dxd99kz9bbgm4/gwSe5wf82pTzLzd2GBZy8XfzZPLF2ft9OfP6/XZ+7zxv3d7FdN1/3NuNc8M/f2e8kz8/l88I8vvk67L6+/Ns37asKbD96PlQ0w11p/KEmZs+oHr9nnu5N896oyAQAAAABwf1b+I38AAAAAAHw6GWAGAAAAAKAVA8wAAAAAALRSaq0PnaG1brdbvawbAAAAAGC1Sinvaq3d2XZPMAMAAAAA0MqThw4AAAAkp3v90czWaHK2nhyO151nYzQz3EyOks7J/GNsJVnL8VRbb7CfJFn2m3+PMc/ZzpwMs8aZbjLJvJ3j9Ab795tnuDm98TV5mrWbZElWVJ9JrvdUnwtbV9df6U/DzXReXm4+22+Sy76T3KE/P8Y8jVxn61ebZ3MlubiGz8bXaa3Rd9rmOdu55rwTzb59Q7++a39OMvX50/RmuJvOy7mX8orJtbuX6/VI8nxyOt1/DmfWX7lmC67Vszmf3c8HvTv350lfauaa+ruRXGTqnFyt2/NBb2r5Q6/PbJ7beDPvM3ww/bdl96Td5/Njy9P8/DnMNX/flzHYzOBg1I+8+eD98AQzAAAAAACtGGAGAAAAAKAVA8wAAAAAALRSaq0PnaG1brdbvUsFAAAAAGC1Sinvaq3d2XZPMAMAAAAA0IoBZgAAAAAAWnny0AEAAIDkdK+fs53kcLx8no35Gw43R9Oj6ebOyeX8do7TG+xPrV/21XKnb/s5W39Eefb6lwtbl7Nn65fzh7M5B5tTWRq7JUnWGrnuK0/TpH7n2RjV6Sh5djK9zVqOp5ZXkadZozfN+owzrbw+44MvvFYz12lmt7mZHkue54NeknuoT5Kznek8TVfuv5n7rnNymW0tx3n6YvvOeebVaG6WRr3u63p9fDonT8Pcz6NJTZKpfv1qfI2aPvQ8p28v87xev8zQOdicugaLPl9u0qo/N/rLYWPd+exnTnLl70VT52T0N2Oi1f3eqM/U37Hh7sW1aN4vs26q0318Pp+tJ28Gu0sdJ0l2T65e0w8+z5z+3PSZg91Ff2Zv9HzQWzoP7XiCGQAAAACAVgwwAwAAAADQigFmAAAAAABaKbXWh87QWrfbrd6lAgAAAACwWqWUd7XW7my7J5gBAAAAAGjlyUMHAAAAxr/q3vg198PGuvNsXC4MN0fTo8umzsnl/HYuf9G9N9i/mG/1K/OPLU/T1ihX0+FstmbGo+mmzsnFPy9rOc7TF9tL5znbudp+OJ7eNscky6tBb6rtTvVZpjYLcjXr83zQWz7P2/5FhsNx25XzT/pOMrcus1nWxn2pN9j/8PNMrtfW/PXNe+4i53AznZdXt513j921/8xmSW6o27hek3u/eW+1rs/W9LnnnXe2Htvj893kQ8/zyWk/b+bcz7sHX3ur8913nmZ/nv3smTjMgr8dSXJ02XdmPwvb5PnkdJRnVTW689+vWQs+ByYOfnp3bvuzk5afz48tz9t+DtY3Fm8w2Fy87hq7J+0+f2jHE8wAAAAAALRigBkAAAAAgFYMMAMAAAAA0IoBZgAAAAAAWim11ofO0Fq3261e1g0AAAAAsFqllHe11u5suyeYAQAAAABo5clDBwAAAJLTvf78FVuXs2fro+lhkvNsJIPNdE6mNkmSrOU4SdIb7F+0LfvNvw8pz9n6KEMmOYabl+uPMjdTkjwf9O6eZ+bAzSwTVzItyLWW44sa3Ut9tqav0dwsR6PJout2b3nGB5/kaTpszF/kO7pse3byKc1z0/WZc22affY6d6rPvFzD3cv1C/rMddnucn+9Xp/+fBk3T5l8xtzkzv1nkme4e3EPP2iemzQ+H8+zMX+b4WY6Ly8XXw169/J5ONuHMi9D496a1HK7cV9NrKQ+t6nNOON91ef1zlK7Td93s46SwcHonvvg87zt52w9eXPddZhn5u9q8zpt5fIzyZsP3g9PMAMAAAAA0IoBZgAAAAAAWjHADAAAAABAK6XW+tAZWut2u9W7VAAAAAAAVquU8q7W2p1t9wQzAAAAAACtGGAGAAAAAKCVJw8dAAAASE73+jnbuVw+HE/PszGaGW6Opkfz9++cjKZbSdZynCTpDfYv1i/7arnHmCdbydn6dKaLPLMm+RoZJ5mSZHsm013rc2OeZq45NeucjDLdV54bs8ypzyRHcnnd7pJnytZoMrl+U/nGNWlenzm75vmgd9H2qckzJ8esqevYuO8m+SZ9eaI32L+3++vK+ZtmPgPu+/6a52ynca3m3EuLatLUuj6PMM/sZ9DERaYZuwdfO/U5vMi91GemPyXJm3l9aU6f3sr93F/L1ifDzQw+17vaPuM+/l7c5DBzMg42pxY7J8mrQe/R5Hl2Mrpuy+b55LR/u7+fTcPNm7dJkqPkxc7TpfLQjieYAQAAAABoxQAzAAAAAACtGGAGAAAAAKCVUmt96Aytdbvduuy7XQAAAAAAWE4p5V2ttTvb7glmAAAAAABaefLQAQAAgMtfvW9q/sr7Yeb8wvpwMzm6XOycjKZbM8dZy3Gevtj+VOSZZDgct09lGMz/VflnJ1fPP9Eb7CfJ0r96P5unmelKruTqL94fTS92TkZ1ej7o3UuehVkmOWbOnyyuU2+w3zrPhXEnOFufv/3hJOeCfJO+tJ3jT2eeRq5JposMydx7q3lfNft0kvb3+8z5k9v1n5vusfu6Xsko30W24W46L69+xszLsYo8V2r1nvJ8ctrPm2ws/My7jdlrNvF80GtXn0V9N7nSf2c1+3Pzc2fiIeqTzK9R2/oc7GxMtXUO7pbt1fhvRdLu78VjyvPJ6YLPwzkOM+f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",
      "text/plain": [
       "<Figure size 1440x3600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# define Matplotlib figure and axis\n",
    "fig, ax = plt.subplots(figsize=(20, 50))\n",
    "# create simple line plot\n",
    "ax.plot([0, 0], [0, 0])\n",
    "\n",
    "labels = np.array(get_true_label_from_bd_dataset(visual_dataset))\n",
    "custom_palette = sns.color_palette(\"hls\", np.unique(labels).shape[0])\n",
    "if poi_indicator.sum() > 0:\n",
    "    custom_palette.append((0.0, 0.0, 0.0))  # Black for poison samples\n",
    "\n",
    "start_x0 = 0\n",
    "height = 1\n",
    "width = 1\n",
    "max_num_neuron = df.Neuron.max()\n",
    "\n",
    "for module_name in module_visual:\n",
    "    print(f'ploting {module_name}')\n",
    "    y_0 = 0\n",
    "    layer_info = df[df.layer == module_name]\n",
    "    total_neuron = layer_info.shape[0]\n",
    "    for neuron_i in range(total_neuron):\n",
    "        x_0 = start_x0\n",
    "        base_row = layer_info.iloc[neuron_i]\n",
    "        for i in range(len(label_set)):\n",
    "            ax.add_patch(Rectangle((x_0, y_0), width*base_row[f'percent_{i}'], height,\n",
    "                         facecolor=custom_palette[i],\n",
    "                         fill=True,\n",
    "                         lw=5,\n",
    "                         alpha=0.8))\n",
    "\n",
    "            x_0 += width*base_row[f'percent_{i}']\n",
    "        y_0 += 1.5*height\n",
    "    start_x0 += 1.5*width\n",
    "x_loc = [0.5*width+1.5*width*i for i in range(len(module_visual))]\n",
    "y_loc = [0.5*height+1.5*height*i for i in range(max_num_neuron)]\n",
    "\n",
    "ax.set_xlim(xmin=-0.5*width, xmax=1.5*width*(len(module_visual)+1))\n",
    "ax.set_ylim(ymin=-0.5*height, ymax=1.5*height*(max_num_neuron+1))\n",
    "ax.set_xticks(x_loc, module_visual, rotation=270)\n",
    "ax.set_yticks(y_loc[::10], np.arange(max_num_neuron)[::10])\n",
    "ax.set_title(f'Distribution of Top-{num_image} Images')\n",
    "ax.set_ylabel('Neuron')\n",
    "ax.set_xlabel('Layer')\n",
    "\n",
    "classes = args.class_names\n",
    "if poi_indicator.sum() > 0:\n",
    "    classes +=  [\"poisoned\"]\n",
    "    \n",
    "# map the label to class name in the order of colors/indexes\n",
    "label_class = [classes[i].capitalize() for i in label_set]\n",
    "legend_elements = [Patch(facecolor=custom_palette[i],\n",
    "                         label=label_class[i]) for i in range(len(label_class))]\n",
    "\n",
    "ax.legend(handles=legend_elements, loc='upper center', bbox_to_anchor=(\n",
    "    0.5, 1.02), ncol=len(label_class), fancybox=True, shadow=True)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13 (default, Oct 21 2022, 23:50:54) \n[GCC 11.2.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
